Kenneth Grogan

Vietnam's forest transition in retrospect: demonstrating weaknesses in business-as-usual scenarios for REDD.

One of the prerequisites of the REDD+ mechanism is to effectively predict business-as-usual (BAU) scenarios for change in forest cover. This would enable estimation of how much carbon emission a project could potentially prevent and thus how much carbon credit should be rewarded. However, different factors like forest degradation and the lack of linearity in forest cover transitions challenge the accuracy of such scenarios. Here we predict and validate such BAU scenarios retrospectively based on forest cover changes at village and district level in North Central Vietnam. With the government’s efforts to increase the forest cover, land use policies led to gradual abandonment of shifting cultivation since the 1990s. We analyzed Landsat images from 1973, 1989, 1998, 2000, and 2011 and found that the policies in the areas studied did lead to increased forest cover after a long period of decline, but that this increase could mainly be attributed to an increase in open forest and shrub areas. We compared Landsat classifications with participatory maps of land cover/use in 1998 and 2012 that indicated more forest degradation than was captured by the Landsat analysis. The BAU scenarios were heavily dependent on which years were chosen for the reference period. This suggests that hypothetical REDD+ activities in the past, when based on the remote sensing data available at that time, would have been unable to correctly estimate changes in carbon stocks and thus produce relevant BAU scenarios.

Mapping Clearances in Tropical Dry Forests Using Breakpoints, Trend, and Seasonal Components from MODIS Time Series: Does Forest Type Matter?

Tropical environments present a unique challenge for optical time series analysis, primarily owing to fragmented data availability, persistent cloud cover and atmospheric aerosols. Additionally, little is known of whether the performance of time series change detection is affected by diverse forest types found in tropical dry regions. In this paper, we develop a methodology for mapping forest clearing in Southeast Asia using a study region characterised by heterogeneous forest types. Moderate Resolution Imaging Spectroradiometer (MODIS) time series are decomposed using Breaks For Additive Season and Trend (BFAST) and breakpoints, trend, and seasonal components are combined in a binomial probability model to distinguish between cleared and stable forest. We found that the addition of seasonality and trend information improves the change model performance compared to using breakpoints alone. We also demonstrate the value of considering forest type in disturbance mapping in comparison to the more common approach that combines all forest types into a single generalised forest class. By taking a generalised forest approach, there is less control over the error distribution in each forest type. Dry-deciduous and evergreen forests are especially sensitive to error imbalances using a generalised forest model i.e., clearances were underestimated in evergreen forest, and overestimated in dry-deciduous forest. This suggests that forest type needs to be considered in time series change mapping, especially in heterogeneous forest regions. Our approach builds towards improving large-area monitoring of forest-diverse regions such as Southeast Asia. The findings of this study should also be transferable across optical sensors and are therefore relevant for the future availability of dense time series for the tropics at higher spatial resolutions.

Assessing drivers of vegetation changes in drylands from time series of earth observation data

This chapter summarizes methods of inferring information about drivers of global dryland vegetation changes observed from remote sensing time series data covering from the 1980s until present time. Earth observation (EO) based time series of vegetation metrics, sea surface temperature (SST) (both from the AVHRR (Advanced Very High Resolution Radiometer) series of instruments) and precipitation data (blended satellite/rain gauge) are used for determining the mechanisms of observed changes. EO-based methods to better distinguish between climate and human induced (land use) vegetation changes are reviewed. The techniques presented include trend analysis based on the Rain-Use Efficiency (RUE) and the Residual Trend Analysis (RESTREND) and the methodological challenges related to the use of these. Finally, teleconnections between global sea surface temperature (SST) anomalies and dryland vegetation productivity are illustrated and the associated predictive capabilities are discussed.

Assessment of Vegetation Trends in Drylands from Time Series of Earth Observation Data

This chapter summarizes approaches to the detection of dryland vegetation change and methods for observing spatio-temporal trends from space. An overview of suitable long-term Earth Observation (EO) based datasets for assessment of global dryland vegetation trends is provided and a status map of contemporary greening and browning trends for global drylands is presented. The vegetation metrics suitable for per-pixel temporal trend analysis is discussed, including seasonal parameterisation and the appropriate choice of trend indicators. Recent methods designed to overcome assumptions of long-term linearity in time series analysis (Breaks For Additive Season and Trend(BFAST)) are discussed. Finally, the importance of the spatial scale when performing temporal trend analysis is introduced and a method for image downscaling (Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM)) is presented.

Environmental justice research shows the importance of social feedbacks in ecosystem service trade-offs

In this article, we shine a spotlight on approaches to research ecosystem service trade-offs and critically assess their representation of relevant social dynamics. Although studies linking ecosystem services and human well-being have provided theoretical insights into social and ecological trade-offs, we argue that ecosystem services research has paid insufficient attention to “social feedbacks,” people’s cognitive and behavioral responses to change. We demonstrate that augmenting ecosystem services research with environmental justice approaches (exploring perceptions of the distribution of costs and benefits, decision making procedures, and recognition of different values and identities) can more effectively capture important responses to ecosystem governance. Spatial analysis of land use change, mixed-method assessment of multidimensional well-being, and qualitative environmental justice research were applied in three villages adjacent to Nam Et-Phou Louey National Protected Area in northern Laos. Spatial analysis showed that, from 2006 to 2015, forest clearance for cultivation remained stable within the protected area. Well-being assessment revealed the local population benefited from rapidly increasing incomes, asset ownership, and reduced poverty during that time. In combination, spatial and well-being analyses paint a picture of limited trade-offs, despite growing incentives to exploit protected land and resources through cash crops and high-value forest products. In contrast, results from environmental justice research revealed profound trade-offs between conservation and local practices, and highlight governance deficiencies relating to procedure and recognition. Consequently, formal protected area rules were perceived to be illegitimate by many and actively undermined, for example through negotiated access with alternative authorities. We conclude that although well-being research provides an essential foundation to understand diverse attachments to natural resources, the addition of environmental justice research can reveal local perceptions and social feedbacks critical to ecosystem service trade-offs, and highlight pathways to reconcile them through satisfying stakeholders’ diverse, dynamic objectives.

Uncertainty in establishing forest reference levels and predicting future forest-based carbon stocks for REDD+

ABSTRACT Forest reference levels (FRLs) provide a benchmark for assessing reduced emissions from deforestation and forest degradation (REDD+), and they are central to demonstrate additionality of REDD+. Attaining realistic FRLs, however, is challenging, especially in complex mosaic landscapes. We established FRLs in northern Laos for different reference periods and tested them against actual carbon stock changes. Annual time series of Landsat satellite images were used to capture the subtle changes in carbon stocks in complex landscapes characterized by shifting cultivation. We found that FRLs differ considerably depending on the reference period chosen. Abrupt land-use changes occurred when hybrid maize replaced traditional shifting cultivation and forests, and this invalidated carbon stock trends that would have been predicted had the FRL been projected into the future. We conclude that demonstrating additionality of REDD+ in fast developing areas is difficult and that payment systems rewarding potential emission reductions against hypothetical extrapolation of FRLs are unlikely to be a cost-effective strategy.