Genevieve Patenaude

A Multispectral Canopy LiDAR Demonstrator Project

The first demonstration of a multispectral light detection and ranging (LiDAR) optimized for detailed structure and physiology measurements in forest ecosystems is described. The basic principle is to utilize, in a single instrument, both the capacity of multispectral sensing to measure plant physiology [through normalized difference vegetation index (NDVI) and photochemical reflectance index (PRI)] with the ability of LiDAR to measure vertical structure information and generate “hot spot” (specular) reflectance data independent of solar illumination. A tunable laser operated at four wavelengths (531, 550, 660, and 780 nm) was used to measure profiles of the NDVI and the PRI. Laboratory-based measurements were conducted for live trees, demonstrating that realistic values of the indexes can be measured. A model-based analysis demonstrates that the LiDAR waveforms cannot only capture the tree height information but also picks up the seasonal and vertical variation of NDVI inside the tree canopy.

Turning the Tide: How Blue Carbon and Payments for Ecosystem Services (PES) Might Help Save Mangrove Forests

In this review paper, we aim to describe the potential for, and the key challenges to, applying PES projects to mangroves. By adopting a “carbocentric approach,” we show that mangrove forests are strong candidates for PES projects. They are particularly well suited to the generation of carbon credits because of their unrivaled potential as carbon sinks, their resistance and resilience to natural hazards, and their extensive provision of Ecosystem Services other than carbon sequestration, primarily nursery areas for fish, water purification and coastal protection, to the benefit of local communities as well as to the global population. The voluntary carbon market provides opportunities for the development of appropriate protocols and good practice case studies for mangroves at a small scale, and these may influence larger compliance schemes in the future. Mangrove habitats are mostly located in developing countries on communally or state-owned land. This means that issues of national and local governance, land ownership and management, and environmental justice are the main challenges that require careful planning at the early stages of mangrove PES projects to ensure successful outcomes and equitable benefit sharing within local communities.

Integrating remote sensing datasets into ecological modelling: a Bayesian approach

Process‐based models have been used to simulate 3‐dimensional complexities of forest ecosystems and their temporal changes, but their extensive data requirement and complex parameterisation have often limited their use for practical management applications. Increasingly, information retrieved using remote sensing techniques can help in model parameterisation and data collection by providing spatially and temporally resolved forest information. In this paper, we illustrate the potential of Bayesian calibration for integrating such data sources to simulate forest production. As an example, we use the 3‐PG model combined with hyperspectral, LiDAR, SAR and field‐based data to simulate the growth of UK Corsican pine stands. Hyperspectral, LiDAR and SAR data are used to estimate LAI dynamics, tree height and above ground biomass, respectively, while the Bayesian calibration provides estimates of uncertainties to model parameters and outputs. The Bayesian calibration contrasts with goodness‐of‐fit approaches, which do not provide uncertainties to parameters and model outputs. Parameters and the data used in the calibration process are presented in the form of probability distributions, reflecting our degree of certainty about them. After the calibration, the distributions are updated. To approximate posterior distributions (of outputs and parameters), a Markov Chain Monte Carlo sampling approach is used (25 000 steps). A sensitivity analysis is also conducted between parameters and outputs. Overall, the results illustrate the potential of a Bayesian framework for truly integrative work, both in the consideration of field‐based and remotely sensed datasets available and in estimating parameter and model output uncertainties.

Delineation of individual tree crowns for LiDAR tree and stand parameter estimation in Scottish woodlands

There is an increasing need for accurate forest inventories to assist forest managers and decision makers in the planning of the forest resources. Airborne LiDAR methods enable the construction of Tree Canopy Models (TCM) at a fine resolution, which allow the delineation of individual tree crowns. This information can be useful for the prediction of forest parameters such as top height, basal area, standing volume and biomass. In this paper, we present a comparative analysis of the algorithms developed independently by Gougeon (1995), Popescu (2003) and Weinacker (2004a) for delineating individual tree crowns and as a means to extract forest parameters. The comparison was achieved as follow. Firstly, the algorithms were tested in their efficiency for delineating tree crowns. Secondly, single tree parameters were estimated using the crown delineation and finally, stand parameters were estimated by averaging single tree parameters. Results of the three algorithms were compared to each other and to field measurement for validation. The results showed that the algorithm by Popescu was the most suitable method to delineate crowns with 89% accuracy. However only 72% were linked with actual trees measured in the field. The algorithm by Popescu was the most suitable to estimate individual tree height with s RMSE (%) of 1.93 m (8.1%). The algorithm by Gougeon was the most suitable to estimate individual crown diameter and stem diameter with a RMSE (%) of 1.81 m (31.7%) and 7.05 cm (21.8%) respectively. The algorithm by Popescu was the most suitable to estimate top height with a RMSE (%) of 0.94 m (3.8%). Finally, the algorithm by Weinacker was the most suitable to estimate stand basal area and volume with a RMSE (%) of 9.10 m2/ha (24.3%) and 119.7 m3/ha (29.4%) respectively.

Airborne SAR monitoring of tree growth in a coniferous plantation

This paper examines three empirically based methods of monitoring forest growth between 1991 and 2000 from airborne synthetic aperture radar (SAR). In the first method, height change and volume change between 1991 and 2000 were estimated from the mean L‐band stand backscatter difference between the two dates. Height change and volume change over the 9‐year period were estimated with an accuracy of 0.23 m and 15 m3 ha−1, respectively, when the stands were below saturation point for the first date. The accuracy of the results was lower for stands beyond saturation in both data sets. In the second method, the height change is calculated from the estimated stand height in 2000 minus the estimated stand height in 1991. The second method produced poorer results than the first method, but better results than predicted by the error propagation equation. The difference between the observed accuracy and the expected error (based on the error propagation equation) appears to be due to a systematic bias in both the 1991 and 2000 estimates, as the residuals are correlated for stands below 20 years old (r = 0.71 for stand volume residuals). The third experiment investigates the utility of data from two dates to classify the stands into three age classes. The results show that, with two images separated by 9 years, 85% of stands were correctly classified compared with 69% for a single date L‐HV image.

Use of LiDAR in the conservation management of the endangered red squirrel (Sciurus vulgaris L.)

Abstract LiDAR remote sensing allows the direct retrieval of vegetation structure parameters and has been widely used to assess habitat quality for various species. The aim of this study is to test whether LiDAR can help in providing estimates of habitat suitability over larger scales and inform conservation management planning in stronghold areas of an endangered forest mammal, the red squirrel (Sciurus vulgaris L.). The Eurasian red squirrel is endangered in the UK and under strict legal protection. Hence, long-term habitat management is a key goal of the UK conservation strategy. This involves understanding habitat preferences of the species. In a previous study, we demonstrated the importance of forest structure for red squirrels’ habitat preference. We used a general linear model (GLM) to relate the distribution and abundance of squirrel feeding signs to mean canopy closure, mean tree height, and the total number of trees at the plot level. However, this analysis was limited to a few sample areas. In the current study, we implement the GLM using LiDAR-derived explanatory variables in Abernethy Forest. Results suggest that when forest structure is considered, only 27% of the total forest area is highly suitable for red squirrel. Implications for management are discussed.

Variance-based sensitivity analysis of a wind risk model - Model behaviour and lessons for forest modelling

We submitted the semi-empirical, process-based wind-risk model ForestGALES to a variance-based sensitivity analysis using the method of Sobo for correlated variables proposed by Kucherenko etal. (2012). Our results show that ForestGALES is able to simulate very effectively the dynamics of wind damage to forest stands, as the model architecture reflects the significant influence of tree height, stocking density, dbh, and size of an upwind gap, on the calculations of the critical wind speeds of damage. These results highlight the importance of accurate knowledge of the values of these variables when calculating the risk of wind damage with ForestGALES. Conversely, rooting depth and soil type, i.e. the model input variables on which the empirical component of ForestGALES that describes the resistance to overturning is based, contribute only marginally to the variation in the outputs. We show that these two variables can confidently be fixed at a nominal value without significantly affecting the models predictions. The variance-based method used in this study is equally sensitive to the accurate description of the probability distribution functions of the scrutinised variables, as it is to their correlation structure. The Sobo method for correlated variables is applied to a complex wind-risk model.The results are interpreted from the viewpoints of model users and modellers.The variance-based approach is sensitive to the variables correlation structure.Rooting depth and soil type provide minor contribution to the outputs variance.ForestGALES models the dynamics of wind damage to forest stands very effectively.

Academic and research capacity development in Earth observation for environmental management

Sustainable environmental management is one of the key development goals of the 21st century. The importance of Earth observation (EO) for addressing current environmental problems is well recognized. Most developing countries are highly susceptible to environmental degradation; however, the capacity to monitor these changes is predominantly located in the developed world. Decades of aid and effort have been invested in capacity development (CD) with the goal of ensuring sustainable development. Academics, given their level of freedom and their wider interest in teaching and knowledge transfer, are ideally placed to act as catalyst for capacity building. In this letter, we make a novel investigation into the extent to which the EO academic research community is engaged in capacity development. Using the Web of Knowledge publication database (http://wok.mimas.ac.uk), we examined the geographical distribution of published EO related research (a) by country as object of research and (b) by authors’ country of affiliation. Our results show that, while a significant proportion of EO research (44%) has developing countries as their object of research, less than 3% of publications have authors working in, or affiliated to, a developing country (excluding China, India and Brazil, which not only are countries in transition, but also have well established EO capacity). These patterns appear consistent over the past 20 years. Despite the wide awareness of the importance of CD, we show that significant progress on this front is required. We therefore propose a number of recommendations and best practices to ease collaboration and open access.

The impacts of Tanzania's natural resource management programmes for ecosystem services and poverty alleviation

SUMMARY For Tanzanias emerging REDD+ policy to successfully build on existing community based natural resource management structures, a critical analysis of the outcomes of existing policy approaches for ecosystem services and poverty alleviation is needed. Our research addresses this aim, and provides an analysis of Tanzanias four prominent natural resource management programmes. Our analysis focuses on impacts on poverty alleviation (PA) and ecosystem services (ES). The research, conducted between June 2011 and April 2012, involved a 10-months period in Tanzania engaging with key policy and academic experts and reviewing relevant literature. Programmes relating to forests, environment and development as well as to community-based natural resource management in Tanzania were reviewed. These were further analysed based on governance themes, namely their level of: (1) decentralisation; (2) intersectoral integration; (3) community access to resources; (4) operational simplicity; and (5) equitable benefit sharing (both within the local community/user groups, and between governance levels). We derive a relative assessment of the degree of influence (High, Intermediate, Low) and nature of influence (Positive, Neutral, Negative) of the programmes on ES and PA outcomes. Building on this analysis, we provide contextual insights and recommendations specific to Tanzania for nascent policy initiatives (e.g. REDD+). The need for such contextual recommendations has been profoundly stressed.

Addressing the forest science versus investments nexus: can a more holistic understanding of risks bridge the gap?

613 ISSN 1758-3004 10.4155/CMT.11.65