Browsing by Author "Otieno, Jared"

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NAFORMA: National forest resources monitoring and assessment of Tanzania Mainland
(FAO, 2022) Rajala, Tuomas; Heikkinen, Juha; Gogo, Sophia; Ahimbisibwe, Joyce; Bakanga, Geofrey; Chamuya, Nurdin; Perez, Javier Garcia; Kilawe, Edward; Kiluvia, Shani; Morales, David; Nzunda, Emmanuel; Otieno, Jared; Sawaya, Jonathan; Vesa, Lauri; Zahabu, Eliakimu; Henry, Matieu
hree options for the sampling design of the field plot clusters of NAFORMA II biophysical survey are compared in this report. Option 1 consists of re-measuring all NAFORMA I field sample plots (3 205 clusters) and Option 2 of re-measuring only those that were established as permanent (848 clusters). The recommended Option 3 is a compromise between these two “extreme” options: Re-measure a subset (1 405 clusters) of NAFORMA I field sample plots including (almost) all permanent clusters and a carefully selected set of other NAFORMA I field plot clusters to obtain a uniform sample within each TFS zone. Design Option 3 has the following features: • • • • Sampling intensity is uniform within each TFS zone. This makes it simple to use the data. For example, mean volumes can be estimated by averages over the plots. The selected clusters are well-spread over the target population. The anticipated precision of land-class area and mean wood volume relative to sample size is nearly as good as that of NAFORMA I. All proposed clusters were measured in NAFORMA I, which enables precise estimation of change based on repeated measurements. The costs and precision were anticipated by utilizing NAFORMA I field data, information about subsequent improvements in the road network, and changes in land-use using satellite imaging derived land-class maps.
A report to the food and agriculture organization of the united nations (FAO) in support of sampling study for National Forestry Resources Monitoring and Assessment (NAFORMA) in Tanzania
(FAO, 2010) Tomppo, Erkki; Katila, Matti; Makisara, Kai; Per ̈asaari, Jouni; Malimbwi, Rogers; Chamuya, Nurudin; Otieno, Jared; Dalsgaard, Søren; Leppanen, Mikko
The Food and Agriculture Organization of the United Nations (FAO) has collected global level forest in- formation since 1947. UN-ECE/FAO (United Nations Economic Commission for Europe/FAO) has co- operated with FAO in collecting and publishing the Temperate and Boreal region data (UN-ECE/FAO, 2000). The most recent report, FRA 2005 was published in late 2005 (FAO 2005). Forest resources assessment is based mainly on National Forest Inventories (NFI) conducted at national and sub-national levels. Countries are planning and conducting the inventories on the basis of their own information needs and traditions. Some countries have long traditions, from the beginning of 1920s, while other countries have conducted just one inventory or are even planning the first sampling based inventory, or at a global level, are lacking even the first inventory. While some inventories are wood production oriented, some other inventories are targeted to produce information about non-wood goods and services, or are multi-purpose inventories. Most inventories collect information on the same base variables; however, some inventories collect some hundreds of parameters measured on the field (Tomppo and Andersson 2008). During the past 10 years, The Forestry Department of FAO has invested substantial resources to develop a programme of support to national forest monitoring and assessment (NFMA) (Saket et al. 2010). NFMA operates is mainly that of countries in development. Technical, financial and institutional capacity are often needed.
A sampling design for a large area forest inventory: case Tanzania
(NRC Research Press, 2014-04-21) Tomppo, Erkki; Malimbwi, Rogers; Katila, Matti; Mäkisara, Kai; Chamuya, Nurdin; Zahabu, Eliakimu; Otieno, Jared; Henttonen, Helena M.
Methods for constructing a sampling design for large area forest inventories are presented. The methods, data sets used, and the procedures are demonstrated in a real setting: constructing a sampling design for the first national forest inventory for Tanzania. The approach of the paper constructs a spatial model of forests, landscape, and land use. Sampling errors of the key parameters as well as the field measurement costs of the inventory were estimated using sampling simulation on data. Forests and land use often vary within a country or an area of interest, implying that stratified sampling is an efficient inventory design. Double sampling for stratification was taken for the statistical framework. The work was motivated by the approach used by The Food and Agriculture Organization of the United Nations (FAO) in supporting nations to establish forest inventories. The approach taken deviates significantly from the traditional FAO approaches, making it possible to calculate forest resource estimates at the subnational level without increasing the costs.