David Coomes Flashcards
Ecology from 1000 metres up: airborne remote sensing of processes in human-modified tropical forests
Multispectral imagery. Reflectance. How green is the planet? Hyperspectral sensing. 400 wavebands show reflectances of lignin, cellulose, water content and phosphorus
2 meter pixels
LIDAR - airborne laser scanning
High-resolution state-wide carbonstocks
6.3Pg carbon - yr-1 emitted
2.1Pg in ocean 2.4 in forests
Restoration opportunity in logged areas. Artificial intelligence for carbon stock mapping.
Carbon maps from space. Topography and carbon stocks. Wood density correlated with species density.
Global warming increases dry air, plants are stressed
Increased mortality
2015 el nino in Malaysia
Young forests grew rapidly, tall forests less so. Edges have big effects - less growth. Drier hilltops are more negatively affected than wetter valleys.
Indentifying, protecting and restoring habitats
Carbon maps reveal the need to conserve full riparian areas
Need to be WIDE areas
for Beta diversitty and species variation
Can measure soil carbon instead of aboveground carbon
using spectrometer and phenolics
A critique of general allometry-inspired models for estimating forest carbon density from airborne LiDAR
Spriggs et al., 2019
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.
Above ground biomass estimation in an African tropical forest with lidar and hyperspectral data
Laurin et al 2019
The estimation of above ground biomass in forests is critical for carbon cycle modeling and climate change mitigation programs. Small footprint lidar provides accurate biomass estimates, but its application in tropical forests has been limited, particularly in Africa. Hyperspectral data record canopy spectral information that is potentially related to forest biomass. To assess lidar ability to retrieve biomass in an African forest and the usefulness of including hyperspectral information, we modeled biomass using small footprint lidar metrics as well as airborne hyperspectral bands and derived vegetation indexes. Partial Least Square Regression (PLSR) was adopted to cope with multiple inputs and multicollinearity issues; the Variable of Importance in the Projection was calculated to evaluate importance of individual predictors for biomass. Our findings showed that the integration of hyperspectral bands (R2 = 0.70) improved the model based on lidar alone (R2 = 0.64), this encouraging result call for additional research to clarify the possible role of hyperspectral data in tropical regions. Replacing the hyperspectral bands with vegetation indexes resulted in a smaller improvement (R2 = 0.67). Hyperspectral bands had limited predictive power (R2 = 0.36) when used alone. This analysis proves the efficiency of using PLSR with small-footprint lidar and high resolution hyperspectral data in tropical forests for biomass estimation. Results also suggest that high quality ground truth data is crucial for lidar-based AGB estimates in tropical African forests, especially if airborne lidar is used as an intermediate step of upscaling field-measured AGB to a larger area.
The giant trees of the
Amazon basin
Coomes
Our analysis of 594 airborne laser tran- sects (375 ha each; WebFigure 1) obtained from airborne laser scanning (ALS) sur- veys conducted within the Amazon basin led to the discovery of a tree with a height of 88.5 m (Figure 1). It is surrounded by seven other trees taller than 80 m and many more above 75 m (WebFigure 2). Our high-resolution ALS surveys across 222,750 ha provided unprece- dented information on the existence of record-breaking tall trees in the Amazon basin. However, the detection of these emergent canopies has implications beyond merely documenting an ecologi- cal novelty. Given the sensitivity of trees in the Amazon to droughts, logging, and forest fragmentation, we urge the protec- tion of these globally important forests.
Changes in leaf functional traits of rainforest canopy trees associated with an El Niño event in Borneo
Nunes et al., 2019
El Niño events generate periods of relatively low precipitation, low cloud cover and high temperature over the rainforests of Southeast Asia, but their impact on tree physiology remains poorly understood. Here we use remote sensing and functional trait approaches—commonly used to understand plant acclimation to environmental fluctuations—to evaluate rainforest responses to an El Niño event at a site in northern Borneo. Spaceborne measurements (i.e. normalised difference vegetation index calculated from Moderate Resolution Imaging Spectroradiometer data) show the rainforest canopy greened throughout 2015, coinciding with a strengthening of the El Niño event in Sabah, Malaysia, then lost greenness in early 2016, when the El Niño was at its peak. Leaf chemical and structural traits measured for mature leaves of 65 species (104 branches from 99 tree canopies), during and after this El Niño event revealed that chlorophyll and carotenoid concentrations were 35% higher in mid 2015 than in mid 2016. Foliar concentrations of the nutrients N, P, K and Mg did not vary, suggesting the mineralisation and transportation processes were unaffected by the El Niño event. Leaves contained more phenolics, tannins and cellulose but less Ca and lignin during the El Niño event, with concentration shifts varying strongly among species. These changes in functional traits were also apparent in hyperspectral reflectance data collected using a field spectrometer, particularly in the shortwave infrared region. Leaf-level acclimation and leaf turnover could have driven the trait changes observed. We argue that trees were not water limited in the initial phase of the El Niño event, and responded by flushing new leaves, seen in the canopy greening trend and higher pigment concentrations (associated with young leaves); we argue that high evaporative demand and depleted soil water eventually caused leaves to drop in 2016. However, further studies are needed to confirm these ideas. Time-series of vegetation dynamics obtained from space can only be understood if changes in functional traits, as well as the quantity of leaves in canopies, are monitored on the ground.
Applications of airborne lidar for the assessment of animal species diversity
Simonson et al., 2014
Habitat structure is important in explaining species diversity patterns for many animal groups. If we couldmap habitat structure over large spatial scales, we could use habitat structure–species diversity (HS–SD) relation-ships to model species diversity and inform conservation planning and management. Traditional approaches formeasuring habitat structure cannot be applied over entire landscapes, but remote sensing tools are now able toovercome this limitation. Here, we explore the potentialof airborne lidar for the assessment and monitoring ofanimal species diversity in terrestrial environments.
Eight out of 15 attributes of habitat structure commonly usedinpublishedstudiesrelate to the vertical dimen-sion of habitat. The core strength of lidar is that it is a vertical profiler, and this technology can be used to deriveestimates of all but one of these structural attributes. Lidar can also be used to improve the measurement of thefour commonly used attributes focusing on the horizontal heterogeneity of habitat patches. The spatial grain andextent of HS–SD studies is usually within the operational capability of airbornelidar; when a vertical measure o fhabitat structure has been employed, this is true in 84% of published studies. The potential efficacy of lidar in thisfield of biodiversity studies is underlined by several published examples oflidar modelling of animal species diver-sity.
We conclude that lidar remote sensing is fit for the purpose of biodiversity assessment and monitoring throughits ability to characterize habitat structure, a key driverof animal species diversity, over large spatial scales.
We advocate wider application of lidar-based HS–SD indicators to help tackle the current biodiversity crisis. In com-bination with other remote sensing products, these indicators may support the implementation and monitoringof environmental legislation, inform gap analyses and the planning of management actions for protected areasand species, and drive greater synergy with forest-based climate change mitigation.
Use of an Airborne Lidar System to Model Plant Species Composition and Diversity of Mediterranean Oak Forests
Simonson et al., 2012
Airborne lidar is a remote‐sensing tool of increasing importance in ecological and conservation research due to its ability to characterize three‐dimensional vegetation structure. If different aspects of plant species diversity and composition can be related to vegetation structure, landscape‐level assessments of plant communities may be possible. We examined this possibility for Mediterranean oak forests in southern Portugal, which are rich in biological diversity but also threatened. We compared data from a discrete, first‐and‐last return lidar data set collected for 31 plots of cork oak (Quercus suber) and Algerian oak (Quercus canariensis) forest with field data to test whether lidar can be used to predict the vertical structure of vegetation, diversity of plant species, and community type. Lidar‐ and field‐measured structural data were significantly correlated (up to r= 0.85). Diversity of forest species was significantly associated with lidar‐measured vegetation height (R2= 0.50, p < 0.001). Clustering and ordination of the species data pointed to the presence of 2 main forest classes that could be discriminated with an accuracy of 89% on the basis of lidar data. Lidar can be applied widely for mapping of habitat and assessments of habitat condition (e.g., in support of the European Species and Habitats Directive [92/43/EEC]). However, particular attention needs to be paid to issues of survey design: density of lidar points and geospatial accuracy of ground‐truthing and its timing relative to acquisition of lidar data.
