There is currently much interest in developing general approaches for mapping forest aboveground carbon density using structural information contained in airborne LiDAR data. The most widely utilized model in tropical forests assumes that aboveground carbon density is a compound power function of top of canopy height (a metric easily derived from LiDAR), basal area and wood density. Here we derive the model in terms of the geometry of individual tree crowns within forest stands, showing how scaling exponents in the aboveground carbon density model arise from the height−diameter (H−D) and projected crown area−diameter (C−D) allometries of individual trees. We show that a power function relationship emerges when the C−D scaling exponent is close to 2, or when tree diameters follow a Weibull distribution (or other specific distributions) and are invariant across the landscape. In addition, basal area must be closely correlated with canopy height for the approach to work. The efficacy of the model was explored for a managed uneven−aged temperate forest in Ontario, Canada within which stands dominated by sugar maple (Acer saccharum Marsh.) and mixed stands were identified. A much poorer goodness−of−fit was obtained than previously reported for tropical forests (R2 = 0.29 vs. about 0.83). Explanations for the poor predictive power on the model include: (1) basal area was only weakly correlated with top canopy height; (2) tree size distributions varied considerably across the landscape; (3) the allometry exponents are affected by variation in species composition arising from timber management and soil conditions; and (4) the C-D allometric power function was far from 2 (1.28). We conclude that landscape heterogeneity in forest structure and tree allometry reduces the accuracy of general power-function models for predicting aboveground carbon density in managed forests. More studies in different forest types are needed to understand the situations in which power functions of LiDAR height are appropriate for modelling forest carbon stocks.
The microclimate mapping challenge
Organisms experience climate at a small scale, where the topography and the vegetation cause microclimates that vary greatly in space and time. The difference in temperature between north and south facing slopes of a mole hill on a hot summer’s day in Europe, for example, is comparable to the temperature difference between the Mediterranean and Scotland. Microclimates are often neglected in ecology and evolution, despite mounting evidence that microclimates matter for ecosystem dynamics and processes, such as the response of organisms to climate change. A key impediment has been the lack of spatial data to map microclimatic variation over large spatial scales and over time. Remote sensing is now offering opportunities to lift this technical barrier, by producing detailed and spatially continuous data-layers that can be used as explanatory variables to model microclimatic conditions over large spatial and temporal scales. We reviewed how these emerging technologies are advancing microclimate modelling and mapping, and highlight some of the opportunities they provide for ecology, conservation and climate change research.
Canopy height mapping with drones
We have assessed the quality of three dimensional forest models produced from drone surveys, and conclude that concerns about their quality for canopy height and carbon measurement are unjustified.
The rise of drones has been explosive. Rapid advances in technology and decreases in price have resulted in products that can be used off-the-shelf to survey forests. Drones take overlapping photos that can be analysed using a piece of software, called structure from motion, that finds the distinctive ‘features’ shared between overlapping images. The location of these so called ‘tie-points’ is then triangulated to build a three-dimensional model of the forest.
This is extremely useful to foresters who want to know how tall their trees are and how much timber or biomass they contain. Until now they have had to choose between traditional field sampling and expensive airborne laser scanning (LiDAR) from piloted aircraft (£10,000 per day) but consumer drones fill a niche somewhere in between, enabling small areas to be surveyed at very little cost (drones can now be bought for less than £1,000). But the quality of the forest models produced by structure from motion has been questioned. The main problem is that the forest floor is generally underexposed in aerial photos, and this means that tree heights could be wrong.
We assessed the quality of these models by mapping logged forest in Indonesia that had already been surveyed by LiDAR and then compared the two approaches. We found a very close relationship between the two sets of tree heights, with 80% of the variation in the LiDAR measurements explained by those from structure from motion but tree heights measured from structure from motion were about 5.5 m shorter than they actually were. This was actually a positive result though because the bias was very consistent making it easy to correct.
We developed a statistical correction that could be applied to our structure from motion forest models and trialled it through comparison to LiDAR at different location surveyed with a different drone. We found that the bias had been removed completely and we were still able to explain 70% of the variation in the LiDAR measurements. The correction also produced accurate biomass measurements. This is game-changing for foresters and restoration managers because it greatly increases the confidence we have in forest surveys made using structure from motion.
Two issues remain that should be understood before running out to buy a drone.
The first is that the greatest uncertainty occurs for short forest thickets, which can be very dense and have flat canopies with very little structural complexity. This makes it difficult to assess forest heights at the early stages of recovery and may reduce the value of our approach in young regenerating forests. However, it should be possible to differentiate between these vegetation types using techniques that can identify patterns and colours in leaves and branches. In fact, our PhD student, Jon Williams, is working on exactly this question.
The second issue is that consumer grade drone positioning systems (GPS) are accurate to about 3 m, but these errors accumulate during the triangulation phase of structure from motion, causing bigger errors in forest models. This problem is usually corrected by installing highly visible objects in the survey area and measuring their positions with high accuracy differential GPS (these are called ‘ground control points’) but this is not really an option under closed canopy forest. So we measured the error in models with and without ground control points, and found that although ground control points are necessary to achieve accuracy at small-scales (0.25 ha), they are not necessary when averaging across several hectares.
Canopy structure and topography jointly constrain the microclimate of human‐modified tropical landscapes
Local‐scale microclimatic conditions in forest understoreys play a key role in shaping the composition, diversity and function of these ecosystems. Consequently, understanding what drives variation in forest microclimate is critical to forecasting ecosystem responses to global change, particularly in the tropics where many species already operate close to their thermal limits and rapid land‐use transformation is profoundly altering local environments. Yet our ability to characterize forest microclimate at ecologically meaningful scales remains limited, as understorey conditions cannot be directly measured from outside the canopy. To address this challenge, we established a network of microclimate sensors across a land‐use intensity gradient spanning from old‐growth forests to oil‐palm plantations in Borneo. We then combined these observations with high‐resolution airborne laser scanning data to characterize how topography and canopy structure shape variation in microclimate both locally and across the landscape. In the processes, we generated high‐resolution microclimate surfaces spanning over 350 km2, which we used to explore the potential impacts of habitat degradation on forest regeneration under both current and future climate scenarios. We found that topography and vegetation structure were strong predictors of local microclimate, with elevation and terrain curvature primarily constraining daily mean temperatures and vapour pressure deficit (VPD), whereas canopy height had a clear dampening effect on microclimate extremes. This buffering effect was particularly pronounced on wind‐exposed slopes but tended to saturate once canopy height exceeded 20 m—suggesting that despite intensive logging, secondary forests remain largely thermally buffered. Nonetheless, at a landscape‐scale microclimate was highly heterogeneous, with maximum daily temperatures ranging between 24.2 and 37.2°C and VPD spanning two orders of magnitude. Based on this, we estimate that by the end of the century forest regeneration could be hampered in degraded secondary forests that characterize much of Borneo’s lowlands if temperatures continue to rise following projected trends.
Assessing the Progress of REDD+ Projects towards the Sustainable Development Goals
Almost a decade since the establishment of Reducing Emissions from Deforestation and Degradation (REDD+), this study investigates the extent to which REDD+ projects are delivering on the promise of co-benefits and the elusive ‘triple-win’ for climate, biodiversity, and local communities. The Climate, Community and Biodiversity Alliance (CCB) is among several leading REDD+ certification standards that are designed to support the delivery of social and environmental co-benefits, and ‘socially-just’ carbon. This study uses an in-depth content analysis of 25 subnational REDD+ project documents to assess the extent to which REDD+ project objectives align with Sustainable Development Goals (SDG) targets, and evaluates the reporting of progress towards meeting these objectives. Currently the CCB standards address a relatively small subset of SDG targets. Despite this, we find that REDD+ projects aspire to work on a much broader set of SDG target objectives, thus going beyond what the CCB Standards require for REDD+ validation. However, although reviewed REDD+ projects have these aspirations, very few are actively monitoring impact against the goals. There is a gap between aspiration and reported progress at the goal level, and for each project: on average, only a third of SDGs that are being targeted by REDD+ projects are showing ‘improvement’. The analysis shows which global goals are most frequently targeted, and which are the least. It also allows an analysis of which projects are following through most effectively in terms of monitoring progress towards the SDGs. This assessment provides insights into the priorities of REDD+ project proponents, suggesting that REDD+ has unfulfilled potential to elicit positive change in relation to the SDGs. Our analysis also shows that there is considerable potential for the safeguarding bodies to do more to ensure that real improvements are made, and reported against, aligning REDD+ projects more strongly with global development agendas.
A simple approach to forest structure classification using airborne laser scanning that can be adopted across bioregions
Reliable assessment of forest structural types (FSTs) aids sustainable forest management. We developed a methodology for the identification of FSTs using airborne laser scanning (ALS), and demonstrate its generality by applying it to forests from Boreal, Mediterranean and Atlantic biogeographical regions. First, hierarchal clustering analysis (HCA) was applied and clusters (FSTs) were determined in coniferous and deciduous forests using four forest structural variables obtained from forest inventory data – quadratic mean diameter (QMD), Gini coefficient (GC), basal area larger than mean (BALM) and density of stems (N) –. Then, classification and regression tree analysis (CART) were used to extract the empirical threshold values for discriminating those clusters. Based on the classification trees, GC and BALM were the most important variables in the identification of FSTs. Lower, medium and high values of GC and BALM characterize single storey FSTs, multi-layered FSTs and exponentially decreasing size distributions (reversed J), respectively. Within each of these main FST groups, we also identified young/mature and sparse/dense subtypes using QMD and N. Then we used similar structural predictors derived from ALS – maximum height (Max), L-coefficient of variation (Lcv), L-skewness (Lskew), and percentage of penetration (cover), – and a nearest neighbour method to predict the FSTs. We obtained a greater overall accuracy in deciduous forest (0.87) as compared to the coniferous forest (0.72). Our methodology proves the usefulness of ALS data for structural heterogeneity assessment of forests across biogeographical regions. Our simple two-tier approach to FST classification paves the way toward transnational assessments of forest structure across bioregions.
Estimating aboveground carbon density and its uncertainty in Borneo’s structurally complex tropical forests using airborne laser scanning
Borneo contains some of the world’s most biodiverse and carbon-dense tropical forest, but this 750 000 km2 island has lost 62 % of its old-growth forests within the last 40 years. Efforts to protect and restore the remaining forests of Borneo hinge on recognizing the ecosystem services they provide, including their ability to store and sequester carbon. Airborne laser scanning (ALS) is a remote sensing technology that allows forest structural properties to be captured in great detail across vast geographic areas. In recent years ALS has been integrated into statewide assessments of forest carbon in Neotropical and African regions, but not yet in Asia. For this to happen new regional models need to be developed for estimating carbon stocks from ALS in tropical Asia, as the forests of this region are structurally and compositionally distinct from those found elsewhere in the tropics. By combining ALS imagery with data from 173 permanent forest plots spanning the lowland rainforests of Sabah on the island of Borneo, we develop a simple yet general model for estimating forest carbon stocks using ALS-derived canopy height and canopy cover as input metrics. An advanced feature of this new model is the propagation of uncertainty in both ALS- and ground-based data, allowing uncertainty in hectare-scale estimates of carbon stocks to be quantified robustly. We show that the model effectively captures variation in aboveground carbon stocks across extreme disturbance gradients spanning tall dipterocarp forests and heavily logged regions and clearly outperforms existing ALS-based models calibrated for the tropics, as well as currently available satellite-derived products. Our model provides a simple, generalized and effective approach for mapping forest carbon stocks in Borneo and underpins ongoing efforts to safeguard and facilitate the restoration of its unique tropical forests.
Effect of Tree Phenology on LiDAR Measurement of Mediterranean Forest Structure
Retrieval of forest biophysical properties using airborne LiDAR is known to differ between leaf-on and leaf-off states of deciduous trees, but much less is understood about the within-season effects of leafing phenology. Here, we compare two LiDAR surveys separated by just six weeks in spring, in order to assess whether LiDAR variables were influenced by canopy changes in Mediterranean mixed-oak woodlands at this time of year. Maximum and, to a slightly lesser extent, mean heights were consistently measured, whether for the evergreen cork oak (Quercus suber) or semi-deciduous Algerian oak (Q. canariensis) woodlands. Estimates of the standard deviation and skewness of height differed more strongly, especially for Algerian oaks which experienced considerable leaf expansion in the time period covered. Our demonstration of which variables are more or less affected by spring-time leafing phenology has important implications for analyses of both canopy and sub-canopy vegetation layers from LiDAR surveys.
Topography shapes the structure, composition and function of tropical forest landscapes
Topography is a key driver of tropical forest structure and composition, as it constrains local nutrient and hydraulic conditions within which trees grow. Yet, we do not fully understand how changes in forest physiognomy driven by topography impact other emergent properties of forests, such as their aboveground carbon density (ACD). Working in Borneo – at a site where 70‐m‐tall forests in alluvial valleys rapidly transition to stunted heath forests on nutrient‐depleted dip slopes – we combined field data with airborne laser scanning and hyperspectral imaging to characterise how topography shapes the vertical structure, wood density, diversity and ACD of nearly 15 km2 of old‐growth forest. We found that subtle differences in elevation – which control soil chemistry and hydrology – profoundly influenced the structure, composition and diversity of the canopy. Capturing these processes was critical to explaining landscape‐scale heterogeneity in ACD, highlighting how emerging remote sensing technologies can provide new insights into long‐standing ecological questions.
Blind image fusion for hyperspectral imaging with the directional total variation
Hyperspectral imaging is a cutting-edge type of remote sensing used for mapping vegetation properties, rock minerals and other materials. A major drawback of hyperspectral imaging devices is their intrinsic low spatial resolution. In this paper, we propose a method for increasing the spatial resolution of a hyperspectral image by fusing it with an image of higher spatial resolution that was obtained with a different imaging modality. This is accomplished by solving a variational problem in which the regularization functional is the directional total variation. To accommodate for possible mis-registrations between the two images, we consider a non-convex blind super-resolution problem where both a fused image and the corresponding convolution kernel are estimated. Using this approach, our model can realign the given images if needed. Our experimental results indicate that the non-convexity is negligible in practice and that reliable solutions can be computed using a variety of different optimization algorithms. Numerical results on real remote sensing data from plant sciences and urban monitoring show the potential of the proposed method and suggests that it is robust with respect to the regularization parameters, mis-registration and the shape of the kernel.