Understanding changes to aboveground biomass (AGB) in forests undergoing degradation is crucial for accurately and completely quantifying carbon emissions from forest loss and for environmental monitoring in the context of climate change. Monitoring forest degradation as compared to deforestation presents technical challenges because degradation involves widespread, low-intensity AGB removal under varying temporal dynamics. Charcoal production is a key driver for forest degradation in Africa and is projected to increase in the future years. In Sub-Saharan Africa (SSA), where charcoal production drives widespread ABG removal, the utility of optical remote sensing for degradation quantification is challenged by the large inter-seasonal variation and high complexities in ecosystem structure. Limited field measurements on tree structure and aboveground biomass density (AGBD) in many parts of the SSA also impose constraints. In this study, we present a novel data fusion approach combining 3D forest structure from NASA's GEDI Lidar with optical time-series data from Landsat to quantify biomass losses associated with charcoal-related forest degradation over a 10-year time period. We used machine learning models with Landsat spectral indices from the time period of limited hydric stress (LHS) as predictor variables. By applying the best performing Random Forest (RF) model to LandTrendr-stabilized annual LHS Landsat composites, we produced annual forest AGBD maps from 2007 to 2019 over the Mabalane district in southern Mozambique where the dry forest ecosystem was under active charcoal-related degradation since 2008. The RF model achieved an RMSE value of 7.05 Mg/ha (RMSE% = 42%) and R2 value of 0.64 using a 10-fold cross-validation dataset. We quantified a total AGB loss of 2.12 ± 0.06 Megatons (Mt) over the 10-year period, which is only 6.35 ± 2.56% less than the total loss estimated using field-based data as previously published for the same area and time. In addition to quantifying biomass loss, we constructed annual AGBD maps that enabled the characterization of disturbance and recovery. Our framework demonstrates that fusing GEDI and Landsat data through predictive modeling can be used to quantify past forest AGBD dynamics in low biomass forests. This approach provides a satellite-based method to support REDD+ monitoring and evaluation activities in areas where field data is limited and has the potential to be extended to investigate a variety of different disturbance events.
Using case studies from Namibia and Mozambique, we examine how regulations against hunting impact person–place relationships and affect multidimensional wellbeing in conservation spaces. We combine Amartya Sen's capability approach with theories of place, using Chris de Wet's concept of disemplacement to investigate the ways conservation efforts affect rural quality of life. We find conservation and place-making become incompatible if people are prevented from adapting lifestyles and livelihoods to accommodate changing circumstances. By tracing distinct dimensions of the disemplacement process, we demonstrate the adverse and compounding effects of wildlife regulations associated with nature tourism. Disruptions to economic livelihoods and physical security destabilize person–place bonds that enhance wellbeing. Material losses and economic hardship are accompanied by institutional disruptions that contribute to marginalization and social exclusion. We provide a detailed illustration of how conservation regulations constrain agency and contribute to a growing sense of powerlessness by decreasing local control over wildlife, which consequently weakens place attachment and diminishes wellbeing. Our study demonstrates how people become more multidimensionally impoverished as conservation initiatives change the places they value while simultaneously limiting their capabilities to maintain place attachment.
Poverty is widely understood to be a key factor that increases the propensity for individuals and households to be harmed by climatic shocks and stresses. This review explores recent literature at the nexus of climate change impacts, vulnerability, and poverty. Within this literature, poverty is increasingly recognized as a dynamic and multidimensional condition that is shaped by the interplay of social, economic, political, and environmental processes, individual and community characteristics, and historical circumstances. While climate change is never seen as a sole cause of poverty, research has identified numerous direct and indirect channels through which climatic variability and change may exacerbate poverty, particularly in less developed countries and regions. Recent studies have also investigated the effects of climate change on economic growth and poverty levels, formation of poverty traps, and poverty alleviation efforts. These studies demonstrate that climate change‐poverty linkages are complex, multifaceted, and context‐specific. Priority issues for future work include greater attention to factors that promote resilience of poor populations, a stronger focus on nonmonetary dimensions of poverty, investigation of the impacts of climate change on relative poverty and inequality, and exploration of the poverty impacts of extreme climate change. This article is categorized under: Climate Economics > Economics and Climate Change Climate and Development > Social Justice and the Politics of Development
While remotely sensed images of various resolutions have been widely used in identifying changes in urban and peri-urban environments, only very high resolution (VHR) imagery is capable of providing the information needed for understanding the changes taking place in remote rural environments, due to the small footprints and low density of man-made structures in these settings. However, limited by data availability, mapping man-made structures and conducting subsequent change detections in remote areas are typically challenging and thus require a certain level of flexibility in algorithm design that takes into account the specific environmental and image conditions. In this study, we mapped all buildings and corrals for two remote villages in Mozambique based on two single-date VHR images that were taken in 2004 and 2012, respectively. Our algorithm takes advantage of the presence of shadows and, through a fusion of both spectra- and object-based analysis techniques, is able to differentiate buildings with metal and thatch roofs with high accuracy (overall accuracy of 86% and 94% for 2004 and 2012, respectively). The comparison of the mapping results between 2004 and 2012 reveals multiple lines of evidence suggesting that both villages, while differing in many aspects, have experienced substantial increases in the economic status. As a case study, our project demonstrates the capability of a coupling of VHR imagery with locally adjusted classification algorithms to infer the economic development of small, remote rural settlements.
The Earth Observing One (EO-1) satellite was launched in November 2000 as a one year technology demonstration mission for a variety of space technologies. After the first year, it was used as a pathfinder for the creation of SensorWebs. A SensorWeb is the integration of a variety of space, airborne and ground sensors into a loosely coupled collaborative sensor system that automatically provides useful data products. Typically, a SensorWeb is comprised of heterogeneous sensors tied together with an open messaging architecture and web services. SensorWebs provide easier access to sensor data, automated data product production and rapid data product delivery. Disasters are the perfect arena to test SensorWeb functionality since emergency workers and managers need easy and rapid access to satellite, airborne and in-situ sensor data as decision support tools. The Namibia Early Flood Warning SensorWeb pilot project was established to experiment with various aspects of sensor interoperability and SensorWeb functionality. The SensorWeb system features EO-1 data along with other data sets from such satellites as Radarsat, Terra and Aqua. Finally, the SensorWeb team began to examine how to measure economic impact of SensorWeb technology infusion. This paper describes the architecture and software components that were developed along with performance improvements that were experienced. Also, problems and challenges that were encountered are described along with a vision for future enhancements to mitigate some of the problems.
This paper explores the role of aspirational capacity, one cognitive dimension of well-being, as a driver of deforestation among rural smallholders living in or near Mozambique's portion of the Great Limpopo Transfrontier Park. Integrating analyses of remote sensing, socio-economic, and semi-structured interview data within a theoretical framework drawn from Amartya Sen's capability approach, we examine land use decisions in the context of the available options people have to choose from as well as the factors influencing their ultimate choice. Land change detection analysis indicates that more forest conversion occurs within the park, but rates show considerable variation at the community level. We find no association between economic deprivation and deforestation rates. Limited aspirational capacity, manifested in expressions of helplessness and despair, a lack of perceived choices, and fewer agentive pursuits, is one dimension of poverty that does contribute to cropland expansion. Qualitative findings indicate that a more limited capacity to set, pursue, and achieve aspirational goals perpetuates agricultural land use traps and, consequentially, higher deforestation rates. Higher levels of aspirational capacity also contribute to negative conservation outcomes as people adopt the risky but profitable activity of illegal rhino hunting as a means to obtain other valued capabilities.
Protected areas (PAs) serve as a critical strategy for protecting natural resources, conserving biodiversity, and mitigating climate change. While there is a critical need to guide area-based conservation efforts, a systematic assessment of PA effectiveness for storing carbon stocks has not been possible due to the lack of globally consistent forest biomass data. In this study, we present a new methodology utilizing forest structural information and aboveground biomass density (AGBD) obtained from the Global Ecosystem Dynamics Investigation (GEDI) mission. We compare PAs with similar, unprotected forests obtained through statistical matching to assess differences in carbon storage and forest structure. We also assess matching outcomes for a robust and minimally biased way to quantify PA efficacy. We find that all analyzed PAs in Tanzania possess higher biomass densities than their unprotected counterfactuals (24.4% higher on average). This is also true for other forest structure metrics, including tree height, canopy cover, and plant area index (PAI). We also find that community-governed PAs are the most effective category of PAs at preserving forest structure and AGBD – often outperforming those managed by international or national entities. In addition, PAs designated under more than one entity perform better than the PAs with a single designation, especially those with multiple international designations. Finally, our findings suggest that smaller PAs may be more effective for conservation, depending on levels of connectivity. Taken together, these findings support the designation of PAs as an effective means for forest management with considerable potential to protect forest ecosystems and achieve long-term climate goals.
The Southeast Asian rubber boom beginning in the early 2000′s shaped a myriad of socioeconomic and environmental consequences, including deforestation, ecosystem impacts, shifts in community livelihoods, and altered local access to land and resources. Although there has been significant research assessing rubber production in this region, there has been less focus on economic inequality and polarization outcomes in rubber producing areas. This analysis explores the extent to which rubber production growth was associated with changes in rural economic inequality and polarization from 2007/08 to 2012/13, using Lao PDR as a case study. We also investigate the implications of these changes for voluntary sustainability programs focused on rubber production. We achieve this through a synthesis of land-use change and economic data. First, we estimate rubber plantation extent based on Landsat time series data and supervised classification. We combine this with household expenditures data from the Laos Expenditure and Consumption Survey from 2007/08 to 2012/13, conducting Gini decomposition and Duclos Esteban Ray Index calculations to explore economic inequality and polarization in rubber and non-rubber producing areas. Our results indicate that rubber areas experience greater inequality and polarization compared to non-rubber areas. The Northern, Central, and Southern regions experience different economic inequality and polarization outcomes – inequality-enhancing effects appear to be greatest in the South, where large-scale concessions dominate rubber production. We assess the implications of our findings for voluntary rubber sustainability programs, arguing that these programs should address systemic drivers of inequality and polarization, including dispossession from land and forest resources, insufficient worker protections, livelihood vulnerability, and barriers for smallholders. Overall, our results underscore the importance of strong regulation, multi-stakeholder action, and environmental and social performance criteria in rubber production.
