NAFORMA: National Forest Resources Monitoring and Assessment of Tanzania Mainland
Author | : Rajala, T., Heikkinen, J., Gogo, S., Ahimbisibwe, J., Bakanga, G., Chamuya, N., Garcia Perez, J., Kilawe, E., Kiluvia, S., Morales, D., Nzunda, E., Otieno, J., Sawaya, J., Vesa, L., Zahabu, E., Henry, M. |
Publisher | : Food & Agriculture Org. |
Total Pages | : 45 |
Release | : 2022-07-13 |
ISBN-10 | : 9789251364352 |
ISBN-13 | : 9251364354 |
Rating | : 4/5 (354 Downloads) |
Download or read book NAFORMA: National Forest Resources Monitoring and Assessment of Tanzania Mainland written by Rajala, T., Heikkinen, J., Gogo, S., Ahimbisibwe, J., Bakanga, G., Chamuya, N., Garcia Perez, J., Kilawe, E., Kiluvia, S., Morales, D., Nzunda, E., Otieno, J., Sawaya, J., Vesa, L., Zahabu, E., Henry, M. and published by Food & Agriculture Org.. This book was released on 2022-07-13 with total page 45 pages. Available in PDF, EPUB and Kindle. Book excerpt: Three 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.