Tawanda Gara

Last update: 2 December 2013

Title: Modelling spatial patterns of wood volume for improving standing carbon stock estimates in dry forests at the Park/Communal land Interface, Zimbabwe using remotely sensed data.

Summary

One of the most important activities in global environmental change research is to quantify the spatial patterns in wood volume in the context of terrestrial carbon stocks and sinks. Moreover, estimation of wood volume is important in fulfilling the requirements of the United Nations Framework Convention on Climate Change (UNFCCC), particularly REDD-plus (Reducing Emissions from Deforestation and forest Degradation+), which emphasizes on reducing emissions from deforestation and forest degradation and the role of conservation, sustainable management of forests and enhancement of forest carbon stocks. Changes in the forest coverage (conversion of forest land into agricultural land) influence the carbon cycle, thus there is increasing necessity to provide accounting of carbon stocks under REDD.

 Thus, forest conservation at the communal/wildlife under the REDD may offer opportunities for carbon trading and attract possible funding under the post 2008-2012 Kyoto Protocol commitment period thus increasing sink status of forests. However, knowledge on spatial changes of wood volume in dry forests especially at the interface of communal and protected areas remains rudimentary. Estimations of wood volume have largely been based on few experimental plots in forest reserves which are localized without a wider application in larger landscapes. This is often affected by the inhibitive cost of obtaining sufficient ground‐data and also the time and labour required. Thus, there is need to develop methods that lead to fast and robust evaluation of wood volume in savanna dry forests. In this regard, the development of high resolution sensors provides an opportunity to characterise spatial variability in wood volume because of their unique spatial resolution, large-scale observations and multi-band imaging.

Thus in this study we model spatial patterns in wood volume using remotely sensed data at the wildlife / communal lands interface of Gonarezhou National Park and Malipati Communal Lands. Specifically, to accomplish the main objective of the study we will estimate wood volume using remotely sensed indices at field level. Then based on the relationship between wood volume and vegetation indices we will map the spatial variation in wood volume at the wildlife/communal lands interface.

Last update: 2 December 2013