Discrimination of lianas and trees with leaf-level hyperspectral data

Lianas are an important component of the biological diversity in two tropical forests with contrasting moisture regimes in Panama. However, their presence in a tree crown may be a source of confusion in remotely sensed data collected for inventories or assessment of vegetation health. The structural growth form of lianas contrasts with trees in that their proportion of leafy biomass to woody biomass is much higher. In effect, they use trees for structural support and typically form a monolayer of leaves above the crown of the supporting tree. Here, we investigated possible differences between hyperspectral signatures of lianas and trees at the leaf level using pattern recognition techniques. Our method involves principal components analysis followed by training and classification using a selection of supervised parametric and nonparametric classifiers. At a tropical dry forest site (Parque Natural Metropolitano), lianas and trees are distinguishable as groups based on their leaf spectral reflectance characteristics in dry season conditions. Classification was improved using ancillary data on leaf chlorophyll content. Their distinction at this site may be related to drought stress and/or phenological differences between the two groups. At a tropical wet forest site (Fort Sherman), discrimination between the two groups was not as clear. Additional research is required to determine the physiological basis of possible differences as well as to determine if these differences are observable at the canopy level

Costa Rica's payment for environmental services program: intention, implementation, and impact.

We evaluated the intention, implementation, and impact of Costa Rica's program of payments for environmental services (PSA), which was established in the late 1990s. Payments are given to private landowners who own land in forest areas in recognition of the ecosystem services their land provides. To characterize the distribution of PSA in Costa Rica, we combined remote sensing with geographic information system databases and then used econometrics to explore the impacts of payments on deforestation. Payments were distributed broadly across ecological and socioeconomic gradients, but the 1997-2000 deforestation rate was not significantly lower in areas that received payments. Other successful Costa Rican conservation policies, including those prior to the PSA program, may explain the current reduction in deforestation rates. The PSA program is a major advance in the global institutionalization of ecosystem investments because few, if any, other countries have such a conservation history and because much can be learned from Costa Rica's experiences

Carrot or Stick? Modelling How Landowner Behavioural Responses Can Cause Incentive-Based Forest Governance to Backfire

Mitigating the negative impacts of declining worldwide forest cover remains a significant socio-ecological challenge, due to the dominant role of human decision-making. Here we use a Markov chain model of land-use dynamics to examine the impact of governance on forest cover in a region. Each land parcel can be either forested or barren (deforested), and landowners decide whether to deforest their parcel according to perceived value (utility). We focus on three governance strategies: yearly incentive for conservation, one-time penalty for deforestation and one-time incentive for reforestation. The incentive and penalty are incorporated into the expected utility of forested land, which decreases the net gain of deforestation. By analyzing the equilibrium and stability of the landscape dynamics, we observe four possible outcomes: a stationary-forested landscape, a stationary-deforested landscape, an unstable landscape fluctuating near the equilibrium, and a cyclic-forested landscape induced by synchronized deforestation. We find that the two incentive-based strategies often result in highly fluctuating forest cover over decadal time scales or longer, and in a few cases, reforestation incentives actually decrease the average forest cover. In contrast, a penalty for deforestation results in the stable persistence of forest cover (generally >30%). The idea that larger conservation incentives will always yield higher and more stable forest cover is not supported in our findings. The decision to deforest is influenced by more than a simple, “rational” cost-benefit analysis: social learning and myopic, stochastic decision-making also have important effects. We conclude that design of incentive programs may need to account for potential counter-productive long-term effects due to behavioural feedbacks