Energy Access and Reforestation Efforts for Ultra-Poor Households in Southern Malawi

Investigations into energy access in Sub-Saharan Africa often focus on modern energy transitions and electrification projects. However, these studies fail to consider the household level differences in access to energy sources and lack of opportunity to transition to alternative modern fuels. This study uses household-level data to explore household level reforestation efforts as a strategy to improve access to energy sources and improve environmental resilience on a community level. Specifically: Are reforestation efforts in Southern Malawi clustered in space, and do the surrounding land use land cover change classifications or household characteristics influence these efforts? The study, are conducted in southern Malawi with ultra- poor households that receive social cash transfer payments. Therefore, the focus of this study is on the most vulnerable, lowest income households in the community. It is expected that households with limited surrounding forest cover, and those who have received information on agroforestry or sustainable practices would be most likely to participate in reforestation efforts in the form of tree planting. There is observable spatial clustering of village clusters that have been provided information on agroforestry or sustainable practices and household-level tree planting efforts in village clusters, but the two are not found to be spatially correlated. We find that the total land owned by individual households is strongly correlated with tree planting efforts, especially in areas where wood is not primarily collected from plantations. Contrary to the expected result, reforestation efforts are not found to be linked to a current lack of access to energy sources, but are linked to land ownership. This study concludes that participation in un- aided reforestation efforts in southern Malawi may not be a mechanism for households to reduce vulnerability, but are a result of household characteristics like land ownership that enable the ability to plant trees. This paper suggests that promotion efforts should consider other factors that are associated with the decision to reforest to be most effective in promoting sustainable practice.Bachelor of Art

Exploring Data Mining Techniques for Tree Species Classification Using Co-Registered LiDAR and Hyperspectral Data

NASA Goddard’s LiDAR, Hyperspectral, and Thermal imager provides co-registered remote sensing data on experimental forests. Data mining methods were used to achieve a final tree species classification accuracy of 68% using a combined LiDAR and hyperspectral dataset, and show promise for addressing deforestation and carbon sequestration on a species-specific level

Forest resource modelling combining satellite imagery and LiDAR data

El sector forestal tiene un papel relevante en la transición hacia una economía innovadora y eficiente en el consumo de sus recursos. Conocer la disponibilidad espacial de los recursos forestales y su evolución temporal es crítico en la gestión forestal, tanto de los recursos maderables como de los no maderables. El uso de información procedente de sensores remotos se está convirtiendo en una opción cada vez más rigurosa y asequible para el desarrollo de esta tarea. Así, el conocimiento que estas herramientas proporcionan sobre el estado de desarrollo de las masas forestales y la disponibilidad de sus recursos permite hacer frente a los diferentes escenarios futuros que plantea el actual contexto de cambio global. Esta tesis caracteriza y evalúa diferentes recursos forestales mediante la combinación de información continua procedente de imágenes de satélite y datos LiDAR, con diferentes niveles de resolución espacial y espectral. Estos datos, apoyados en trabajo de campo, han sido calibrados y validados, demostrando un gran potencial. Discriminar diferentes especies y tipos de masa, tanto a nivel de árbol individual como de objeto, son objetivos alcanzables mediante el uso adecuado de estas herramientas, disminuyendo la dependencia histórica del trabajo de campo e integrando el cambio de escala en los inventarios tradicionales. Esta tesis desarrolla herramientas robustas capaces de evaluar recursos forestales a gran escala mediante modelos mixtos lineales y técnicas de modelización basadas en aprendizaje automático.Forestry sector plays an important role in the transition towards a new economy, driven by efficient resource consumption. Understanding the spatial distribution of forest resources and its temporal evolution is critical in forest management, both for timber and non-timber resources. Remote sensing information is becoming an increasingly precise and affordable option for the accomplishment of this task. The knowledge provided by these tools regarding stand development and availability of resources enables predicting future global change scenarios. This Doctoral Thesis assesses different forest resources combining continuous information derived from satellite images and LiDAR data at different spatial and spectral resolution levels. This information, supported by field work, has been calibrated and validated, showing a great potential. Species and stand types discrimination, both at individual tree and object levels, can be accomplished with these tools, decreasing the historical dependence of field work and integrating the scale change in traditional inventories. This PhD work aims to develop robust tools able to evaluate large-scale forest resources, by means of linear mixed models and machine learning.Departamento de Producción Vegetal y Recursos ForestalesDoctorado en Conservación y Uso Sostenible de Sistemas Forestale