Can remote sensing be used to support sustainable forestry in Malawi?

Sustainable forest management is a key issue in Malawi. Malawi is a relatively small, resource poor, densely populated country, which in some areas is close to exceeding the energy capacity of the environment to support it. Despite the importance of forestry in Malawi, there is a severe lack of knowledge about the current state of Malawi’s forest resources. Remote sensing has the potential to provide current and historical insights into forest cover change. However, Malawi faces a number of key challenges with regards to in-country remote sensing. These include technical capacity for obtaining accurate and consistent forest area and biomass estimates, with errors at acceptable levels, as well as the necessary supporting capacity development for individuals and institutions. This thesis examines how remote sensing can be used to support sustainable forestry in Malawi, by assessing the use of both optical and Synthetic Aperture Radar (SAR) data for mapping forest cover, forest cover change and aboveground biomass (AGB). L-band SAR data was used to try and establish a relationship between radar backscatter and biomass, which has been achieved many times in other areas. However, no correlations between any field-based forest metric and backscatter explained enough of the variability in the datasets to be used to develop empirical relationships between the variables. There were also differences between my field measured AGB and AGB values predicted by a published backscatter-biomass relationship for African dry forests. The speckle inherent in SAR imagery, the heterogeneity of Malawi’s dominant miombo savanna, and Malawi’s variable topography are likely to have played a significant role in this. Two different MODIS products were investigated for their potential for mapping forest cover change, with regards to potential REDD+ schemes. As part of this, a published equation was used to calculate the break-even point for REDD+ schemes in Malawi, using estimates of forest area and deforestation for the United Nations Forest Resources Assessment 2010. The results of this equation show that measurement error is the most important factor in determining whether or not Malawi can make REDD+ economically viable, particularly at lower levels of deforestation. While neither of the MODIS products were able to produce a verifiable forest cover change map, they do confirm that Malawi is experiencing some level of forest loss, and help to narrow down the range of possible forest loss rates Malawi is experiencing to between 1-3% net forest loss per year. Finally, this thesis examines global trends in the engagement of developing country researchers with global academic remote sensing research, to investigate differences in in-country capacity for monitoring forests using remote sensing. The results of this found that while a significant proportion of Earth observation research (44%) has developing countries as their object of research, less than 3% of publications have authors working, or affiliated to, a developing country (excluding China, India and Brazil, which are not only countries in transition, but have well established EO capacity). These patterns appear consistent over the past 20 years, despite the increasing awareness of the importance of capacity development over this period. Despite inconclusive results from the approaches examined here, remote sensing can play a role in improving understanding about the dynamics of Malawi’s forest resources. There is a need for nationwide accurate, validated forest maps that can be repeated at least on a yearly basis, and remote sensing could produced these without the resources needed to conduct full national ground inventories each year. If remote sensing is to be useful as a forest mapping tool in Malawi, it needs to provide consistent, verifiable and updatable estimates of forest cover and biomass change. This ideally needs to be achieved using free or low cost data, and by using open source or open access software, as this will better enable incountry researchers to conduct on-going forest mapping activities

DDI Economic Development Opportunities: The Lothians

This study aims to identify key areas where Data Driven Innovation (DDI) could drive socio-economic growth across the three Lothian Councils (East Lothian, Midlothian, West Lothian) as part of the Edinburgh and South East Scotland City Region Deal (ESES-CRD), against the background of recent development of a Regional Prosperity Framework (RPF) and Scotland’s National Strategy for Economic Transformation (NSET). This study is the first step in enhancing regional collaborative approaches to realise the potential these DDI opportunities represent, and achieve the regional prosperity and inclusive growth objectives by increasing the wider economic and social resilience across the Lothians

Quantifying small-scale deforestation and forest degradation in African woodlands using radar imagery

Carbon emissions from tropical land-use change are a major uncertainty in the global carbon cycle. In African woodlands, small-scale farming and the need for fuel are thought to be reducing vegetation carbon stocks, but quantification of these processes is hindered by the limitations of optical remote sensing and a lack of ground data. Here, we present a method for mapping vegetation carbon stocks and their changes over a 3-year period in a &gt; 1000 km2 region in central Mozambique at 0.06 ha resolution. L-band synthetic aperture radar imagery and an inventory of 96 plots are combined using regression and bootstrapping to generate biomass maps with known uncertainties. The resultant maps have sufficient accuracy to be capable of detecting changes in forest carbon stocks of as little as 12 MgC ha-1 over 3 years with 95% confidence. This allows characterization of biomass loss from deforestation and forest degradation at a new level of detail. Total aboveground biomass in the study area was reduced by 6.9 +/- 4.6% over 3 years: from 2.13 +/- 0.12 TgC in 2007 to 1.98 +/- 0.11 TgC in 2010, a loss of 0.15 +/- 0.10 TgC. Degradation probably contributed 67% (96.9 +/- 91.0 GgC) of the net loss of biomass, but is associated with high uncertainty. The detailed mapping of carbon stock changes quantifies the nature of small-scale farming. New clearances were on average small (median 0.2 ha) and were often additions to already cleared land. Deforestation events reduced biomass from 33.5 to 11.9 MgC ha-1 on average. Contrary to expectations, we did not find evidence that clearances were targeted towards areas of high biomass. Our method is scalable and suitable for monitoring land cover change and vegetation carbon stocks in woodland ecosystems, and can support policy approaches towards reducing emissions from deforestation and degradation (REDD).</p

Quantifying small-scale deforestation and forest degradation in African woodlands using radar imagery

Carbon emissions from tropical land-use change are a major uncertainty in the global carbon cycle. In African woodlands, small-scale farming and the need for fuel are thought to be reducing vegetation carbon stocks, but quantification of these processes is hindered by the limitations of optical remote sensing and a lack of ground data. Here, we present a method for mapping vegetation carbon stocks and their changes over a 3-year period in a &gt; 1000 km2 region in central Mozambique at 0.06 ha resolution. L-band synthetic aperture radar imagery and an inventory of 96 plots are combined using regression and bootstrapping to generate biomass maps with known uncertainties. The resultant maps have sufficient accuracy to be capable of detecting changes in forest carbon stocks of as little as 12 MgC ha-1 over 3 years with 95% confidence. This allows characterization of biomass loss from deforestation and forest degradation at a new level of detail. Total aboveground biomass in the study area was reduced by 6.9 +/- 4.6% over 3 years: from 2.13 +/- 0.12 TgC in 2007 to 1.98 +/- 0.11 TgC in 2010, a loss of 0.15 +/- 0.10 TgC. Degradation probably contributed 67% (96.9 +/- 91.0 GgC) of the net loss of biomass, but is associated with high uncertainty. The detailed mapping of carbon stock changes quantifies the nature of small-scale farming. New clearances were on average small (median 0.2 ha) and were often additions to already cleared land. Deforestation events reduced biomass from 33.5 to 11.9 MgC ha-1 on average. Contrary to expectations, we did not find evidence that clearances were targeted towards areas of high biomass. Our method is scalable and suitable for monitoring land cover change and vegetation carbon stocks in woodland ecosystems, and can support policy approaches towards reducing emissions from deforestation and degradation (REDD).</p