Development and Extrapolation of a General Light Use Efficiency Model for the Gross Primary Production

The global carbon cycle is one of the large biogeochemical cycles spanning all living and non-living compartments of the Earth system. Against the background of accelerating global change, the scientific community is highly interested in analyzing and understanding the dynamics of the global carbon cycle and its complex feedback mechanism with the terrestrial biosphere. The international network FLUXNET was established to serve this aim with measurement towers around the globe. The overarching objective of this thesis is to exploit the powerful combination of carbon flux measurements and satellite remote sensing in order to develop a simple but robust model for the gross primary production (GPP) of vegetation stands. Measurement data from FLUXNET sites as well as remote sensing data from the NASA sensor MODIS are exploited in a data-based model development approach. The well-established concept of light use efficiency is chosen as modeling framework. As a result, a novel gross primary production model is established to quantify the carbon uptake of forests and grasslands across a broad range of climate zones. Furthermore, an extrapolation scheme is derived, with which the model parameters calibrated at FLUXNET sites can be regionalized to pave the way for spatially continuous model applications

Scale challenges in inventory of forests aided by remote sensing

The impact of changing the scale of observation on information derived from forest inventories is the basis of scale-related research in forest inventory and analysis (FIA). Interactions between the scale of observation and observed heterogeneity in studied variables highlight a dependence on scale that affects measurements, estimates, and relationships between inventory data from terrestrial and remote sensing surveys. This doctoral research defines "scale" as the divisions of continuous space over which measurements are made, or hierarchies of discrete units of study/analysis in space. Therefore, the "scale of observation" (also known as support) refers to that integral of space over which statistics are computed and forest inventory variables regionalized. Given the ubiquitous nature of scale issues, a case study approach was undertaken in this research (Articles I-IV) with the goal to provide fundamental understanding of responses to the scale of observation for specific FIA variables. The studied forest inventory variables are; forest stand structural heterogeneity, forest cover proportion and tree species identities. Forest cover proportion (or simply forest area) and tree species are traditional and fundamental forest inventory variables commonly assessed over large areas using both terrestrial samples and remote sensing data whereas, forest stand structural heterogeneity is a contemporary FIA variable that is increasingly demanded in multi-resource inventories to inform management and conservation efforts as it is linked to biodiversity, productivity, ecosystem functioning and productivity, and used as auxiliary data in forest inventory. This research has two overall aims: 1. To improve the understanding of the association between the scale of observation and observed heterogeneity in inventory of forest stand structural heterogeneity, forest-cover proportions, and identification of tree species from a combination of terrestrial samples and remote sensing data. 2. To contribute knowledge to the estimation of scale-dependence in inventory of forest stand structural heterogeneity, forest-cover proportions, and identification of tree species from a combination of terrestrial samples and remote sensing data. Different scales of observation were considered across the four case studies encompassing individual leaf, crown-part or branch, single-tree crown, forest stand, landscape and global levels of analysis. Terrestrial and remote sensing data sets from a variety of temperate forests in Germany and France were utilized across case studies. In cases where no inventory data were available, synthetic data was simulated at different scales of observation. Heterogeneity in FIA variable estimates was monitored across scales of observation using estimators of variance and associated precision. As too much heterogeneity is hardly interpreted due to a low signal to noise ratio, object-based image analysis (OBIA) methods were used to manage heterogeneity in high resolution remote sensing data before evaluating scale dependence or scaling across observed scales. Similarly, ensemble classification techniques were applied to address methodological heterogeneity across classifiers in a case study on classification of two physically and spectrally similar Pinus species. Across case studies, a dependence on the scale of observation was determined by linking estimates of heterogeneity to their respective scales of observation using linear regression and a combination of geo-statistics and Monte-Carlo approaches. In order to address scale-dependence, thresholds to scale domains were identified so as to enable efficient observation of studied FIA variables and scaling approaches proposed to bridge observations across scales. For scaling, this research evaluated the potential of different regression techniques to map forest stand structural heterogeneity and tree species wall-to-wall from remote sensing data. In addition, radiative transfer modelling was evaluated in the transfer between leaf and crown hyperspectra, and a global sampling grid framework proposed to efficiently link different stages of survey sampling. This research shows that the scale of observation affected all studied FIA variables albeit to varying degrees, conditioned on the spatial structure and aggregation properties of the assessed FIA variable (i.e. whether the variable is extensive, intensive or scale-specific) and the method used in aggregation on support (e.g. mean, variance, quantile etc.). The scale of observation affected measurements or estimates of the studied FIA variables as well as relationships between spatially structured FIA variables. The scale of observation determined observed heterogeneity in FIA variables, affected parameter retrieval from radiative transfer models, and affected variable selection and performance of models linking terrestrial and remote sensing data. On the other hand, this research shows that it is possible to determine domains of scale dependence within which to efficiently observe the studied FIA variables and to bridge between scales of observation using various scaling methods. The findings of this doctoral research are relevant for the general understanding of scale issues in FIA. Research in Article I, for example, informs optimization of plot sizes for efficient inventory and mapping of forest structural heterogeneity, as well as for the design of natural resource inventories. Similarly, research in Article II is applicable in large area forest (or general land) cover monitoring from sampling by both visual interpretation of high resolution remote sensing imagery and terrestrial surveys. This research is also useful to determine observation design for efficient inventory of land cover. Research in Article III contributes in many contexts of remote sensing assisted inventory of forests especially in management and conservation planning, pest and diseases control and in the estimation of biomass. Lastly, research in Article IV highlights scale-related effects in passive optical remote sensing of forests currently understudied and can ultimately contribute to sensor calibration and modelling approaches

Mapping plant diversity and composition across North Carolina Piedmont forest landscapes using LiDAR-hyperspectral remote sensing

Forest modification, from local stress to global change, has given rise to efforts to model, map, and monitor critical properties of forest communities like structure, composition, and diversity. Predictive models based on data from spatially-nested field plots and LiDAR-hyperspectral remote sensing systems are one particularly effective means towards the otherwise prohibitively resource-intensive task of consistently characterizing forest community dynamics at landscape scales. However, to date, most predictive models fail to account for actual (rather than idealized) species and community distributions, are unsuccessful in predicting understory components in structurally and taxonomically heterogeneous forests, and may suffer from diminished predictive accuracy due to incongruity in scale and precision between field plot samples, remotely-sensed data, and target biota of varying size and density. This three-part study addresses these and other concerns in the modeling and mapping of emergent properties of forest communities by shifting the scope of prediction from the individual or taxon to the whole stand or community. It is, after all, at the stand scale where emergent properties like functional processes, biodiversity, and habitat aggregate and manifest. In the first study, I explore the relationship between forest structure (a proxy for successional demographics and resource competition) and tree species diversity in the North Carolina Piedmont, highlighting the empirical basis and potential for utilizing forest structure from LiDAR in predictive models of tree species diversity. I then extend these conclusions to map landscape pattern in multi-scale vascular plant diversity as well as turnover in community-continua at varying compositional resolutions in a North Carolina Piedmont landscape using remotely-sensed LiDAR-hyperspectral estimates of topography, canopy structure, and foliar biochemistry. Recognizing that the distinction between correlation and causation mirrors that between knowledge and understanding, all three studies distinguish between prediction of pattern and inference of process. Thus, in addition to advancing mapping methodologies relevant to a range of forest ecosystem management and monitoring applications, all three studies are noteworthy for assessing the ecological relationship between environmental predictors and emergent landscape patterns in plant composition and diversity in North Carolina Piedmont forests.Doctor of Philosoph

Assessment of forest canopy vertical structure with multi-scale remote sensing: from the plot to the large area

Assessment of vegetation over large, remote and inaccessible areas is an ongoing challenge for land managers in Australia and around the world. This research aimed to develop metrics, techniques and acquisition specifications that are suitable for characterising vegetation across large forested areas. New methods were therefore required to be transferable between forest types as well as robust where forest structure is unknown a priori. Remote sensing techniques were utilised as they have been previously identified as key in forest assessment, owing to their synoptic capture as well as relative cost. Additionally, active remote sensing instruments, such as LiDAR, are capable of sensing 3-dimensional canopy structure. Canopy height and the canopy height profile are fundamental descriptors of forest structure and can be used for estimating biomass, habitat suitability and fire susceptibility. To investigate the ability of remote sensing to characterise vegetation structure across large areas, three key research questions were formulated: I. Which metrics of canopy height and vertical canopy structure are suitable for application across forested landscapes? II. What is the appropriate ALS sampling frequency for attribution of forest structure across different forest types? III. How can plot level estimates of canopy structure be scaled to generate continuous large area maps? A number of inventory measured canopy height metrics were compared with LiDAR analogues, these were shown to be accurate at estimating canopy height and transferable between forest types. Existing techniques for attributing the canopy height profile were found to be inadequate when applied across heterogeneous forests. Therefore a new technique was developed that utilised a nonparametric regression of LiDAR derived gap probability that identified major canopy features e.g. dominant canopy strata and shade tolerant layers beneath. The impact of sampling frequency was assessed using three key descriptors of canopy structure at six sites across Australia covering a range of forest types. The research concluded that forest structure can be adequately characterised with a pulse density of 0.5 pulses m-2 when compared to a higher density acquisition - 10 pulses m-2. At pulse density of <0.5 pulses m-2, the inability to generate an adequate ground surface model lead to poor results, particularly in high biomass forest. The outcomes of this research will allow land managers to specify lower pulse densities when commissioning LiDAR capture, which may result in significant cost savings. Finally, LiDAR derived plot estimates were scaled to an area of 2.9 million hectares of forest, where forest type ranged from short, open woodland to tall, closed canopy rainforest. Attribution was achieved using a two-stage sampling approach utilising the ensemble regression technique Random Forest. Predictor variables included freely available datasets such as Landsat TM and MODIS satellite imagery. Canopy height was estimated with a RMSE of 30% or ~5.5 m when validated with an independent forest inventory dataset. Attribution of the canopy height profile was less successful for a number of reasons, for example, the relatively high spatial variability of shade tolerant vegetation. Inclusion of additional synoptic datasets, such as radar, may improve this in the future

3D Remote Sensing Applications in Forest Ecology: Composition, Structure and Function

Dear Colleagues, The composition, structure and function of forest ecosystems are the key features characterizing their ecological properties, and can thus be crucially shaped and changed by various biotic and abiotic factors on multiple spatial scales. The magnitude and extent of these changes in recent decades calls for enhanced mitigation and adaption measures. Remote sensing data and methods are the main complementary sources of up-to-date synoptic and objective information of forest ecology. Due to the inherent 3D nature of forest ecosystems, the analysis of 3D sources of remote sensing data is considered to be most appropriate for recreating the forest’s compositional, structural and functional dynamics. In this Special Issue of Forests, we published a set of state-of-the-art scientific works including experimental studies, methodological developments and model validations, all dealing with the general topic of 3D remote sensing-assisted applications in forest ecology. We showed applications in forest ecology from a broad collection of method and sensor combinations, including fusion schemes. All in all, the studies and their focuses are as broad as a forest’s ecology or the field of remote sensing and, thus, reflect the very diverse usages and directions toward which future research and practice will be directed

Spatio-temporal and structural analysis of vegetation dynamics of Lowveld Savanna in South Africa

Savanna vegetation structure parameters are important for assessing the biomes status under various disturbance scenarios. Despite free availability remote sensing data, the use of optical remote sensing data for savanna vegetation structure mapping is limited by sparse and heterogeneous distribution of vegetation canopy. Cloud and aerosol contamination lead to inconsistency in the availability of time series data necessary for continuous vegetation monitoring, especially in the tropics. Long- and medium wavelength microwave data such as synthetic aperture radar (SAR), with their low sensitivity to clouds and atmospheric aerosols, and high temporal and spatial resolution solves these problems. Studies utilising remote sensing data for vegetation monitoring on the other hand, lack quality reference data. This study explores the potential of high-resolution TLS-derived vegetation structure variables as reference to multi-temporal SAR datasets in savanna vegetation monitoring. The overall objectives of this study are: (i) to evaluate the potential of high-resolution TLS-data in extraction of savanna vegetation structure variables; (ii) to estimate landscape-wide aboveground biomass (AGB) and assess changes over four years using multi-temporal L-band SAR within a Lowveld savanna in Kruger National Park; and (iii) to assess interactions between C-band SAR with various savanna vegetation structure variables. Field inventories and TLS campaign were carried out in the wet and dry seasons of 2015 respectively, and provided reference data upon which AGB, CC and cover classes were modelled. L-band SAR modelled AGB was used for change analysis over 4 years, while multitemporal C-band SAR data was used to assess backscatter response to seasonal changes in CC and AGB abundant classes and cover classes. From the AGB change analysis, on average 36 ha of the study area (91 ha) experienced a loss in AGB above 5 t/ha over 4 years. A high backscatter intensity is observed on high abundance AGB, CC classes and large trees as opposed to low CC and AGB abundance classes and small trees. There is high response to all structure variables, with C-band VV showing best polarization in savanna vegetation mapping. Moisture availability in the wet season increases backscatter response from both canopy and background classes

Extraction of Vegetation Biophysical Structure from Small-Footprint Full-Waveform Lidar Signals

The National Ecological Observatory Network (NEON) is a continental scale environmental monitoring initiative tasked with characterizing and understanding ecological phenomenology over a 30-year time frame. To support this mission, NEON collects ground truth measurements, such as organism counts and characterization, carbon flux measurements, etc. To spatially upscale these plot-based measurements, NEON developed an airborne observation platform (AOP), with a high-resolution visible camera, next-generation AVIRIS imaging spectrometer, and a discrete and waveform digitizing light detection and ranging (lidar) system. While visible imaging, imaging spectroscopy, and discrete lidar are relatively mature technologies, our understanding of and associated algorithm development for small-footprint full-waveform lidar are still in early stages of development. This work has as its primary aim to extend small-footprint full-waveform lidar capabilities to assess vegetation biophysical structure. In order to fully exploit waveform lidar capabilities, high fidelity geometric and radio-metric truth data are needed. Forests are structurally and spectrally complex, which makes collecting the necessary truth challenging, if not impossible. We utilize the Digital Imaging and Remote Sensing Image Generation (DIRSIG) model, which provides an environment for radiometric simulations, in order to simulate waveform lidar signals. The first step of this research was to build a virtual forest stand based on Harvard Forest inventory data. This scene was used to assess the level of geometric fidelity necessary for small-footprint waveform lidar simulation in broadleaf forests. It was found that leaves have the largest influence on the backscattered signal and that there is little contribution to the signal from the leaf stems and twigs. From this knowledge, a number of additional realistic and abstract virtual “forest” scenes were created to aid studies assessing the ability of waveform lidar systems to extract biophysical phenomenology. We developed an additive model, based on these scenes, for correcting the attenuation in backscattered signal caused by the canopy. The attenuation-corrected waveform, when coupled with estimates of the leaf-level reflectance, provides a measure of the complex within-canopy forest structure. This work has implications for our improved understanding of complex waveform lidar signals in forest environments and, very importantly, takes the research community a significant step closer to assessing fine-scale horizontally- and vertically-explicit leaf area, a holy grail of forest ecology

Remote Sensing of Biophysical Parameters

Vegetation plays an essential role in the study of the environment through plant respiration and photosynthesis. Therefore, the assessment of the current vegetation status is critical to modeling terrestrial ecosystems and energy cycles. Canopy structure (LAI, fCover, plant height, biomass, leaf angle distribution) and biochemical parameters (leaf pigmentation and water content) have been employed to assess vegetation status and its dynamics at scales ranging from kilometric to decametric spatial resolutions thanks to methods based on remote sensing (RS) data.Optical RS retrieval methods are based on the radiative transfer processes of sunlight in vegetation, determining the amount of radiation that is measured by passive sensors in the visible and infrared channels. The increased availability of active RS (radar and LiDAR) data has fostered their use in many applications for the analysis of land surface properties and processes, thanks to their insensitivity to weather conditions and the ability to exploit rich structural and texture information. Optical and radar data fusion and multi-sensor integration approaches are pressing topics, which could fully exploit the information conveyed by both the optical and microwave parts of the electromagnetic spectrum.This Special Issue reprint reviews the state of the art in biophysical parameters retrieval and its usage in a wide variety of applications (e.g., ecology, carbon cycle, agriculture, forestry and food security)

Hybrid modeling of aboveground biomass carbon using disturbance history over large areas of boreal forest in eastern Canada

Le feu joue un rôle important dans la succession de la forêt boréale du nord-est de l’Amérique et le temps depuis le dernier feu (TDF) devrait être utile pour prédire la distribution spatiale du carbone. Les deux premiers objectifs de cette thèse sont: (1) la spatialisation du TDF pour une vaste région de forêt boréale de l'est du Canada (217,000 km2) et (2) la prédiction du carbone de la biomasse aérienne (CBA) à l’aide du TDF à une échelle liée aux perturbations par le feu. Un modèle non paramétrique a d’abord été développé pour prédire le TDF à partir d’historiques de feu, des données d'inventaire et climatiques à une échelle de 2 km2. Cette échelle correspond à la superficie minimale d’un feu pour être inclus dans la base de données canadienne des grands feux. Nous avons trouvé un ajustement substantiel à l’échelle de la région d’étude et à celle de paysages régionaux, mais la précision est restée faible à l’échelle de cellules individuelles de 2 km2. Une modélisation hiérarchique a ensuite été développée pour spatialiser le CBA des placettes d’inventaire à la même échelle de 2 km2. Les proportions des classes de densité du couvert étaient les variables les plus importantes pour prédire le CBA. Le CBA co-variait également avec la vitesse de récupération du couvert au travers de laquelle le TDF intervient indirectement. Finalement, nous avons comparé des estimations de CBA obtenues par télédétection satellitaire avec celles obtenues précédemment. Les résultats indiquent que les proportions des classes de densité du couvert et des types de dépôts ainsi que le TDF pourraient servir comme variables auxiliaires pour augmenter substantiellement la précision des estimés de CBA par télédétection. Les résultats de cette étude ont montré: 1) l'importance d’allonger la profondeur temporelle des historiques de feu pour donner une meilleure perspective des changements actuels du régime de feu; 2) l'importance d'intégrer l’information sur la reprise du couvert après feu aux courbes de rendement de CBA dans les modèles de bilan de carbone; et 3) l'importance de l'historique des feux et de la récupération de la végétation pour améliorer la précision de la cartographie de la biomasse à partir de la télédétection.Fire is as a main succession driver in northeastern American boreal forests and time since last fire (TSLF) is seen as a useful covariate to infer the spatial variation of carbon. The first two objectives of this thesis are: (1) to elaborate a TSLF map over an extensive region in boreal forests of eastern Canada (217,000 km2) and (2) to predict aboveground carbon biomass (ABC) as a function of TSLF at a scale related to fire disturbances. A non-parametric model was first developed to predict TSLF using historical records of fire, forest inventory data and climate data at a 2-km2 scale. Two kilometer square is the minimum size for fires to be considered important enough and included in the Canadian large fire database. Overall, we found a substantial agreement at the scale of both the study area and landscape units, but the accuracy remained fairly low at the scale of individual 2-km2 cells. A hierarchical modeling approach is then presented for scaling-up ABC from inventory plots to the same 2 km2 scale. The proportions of cover density classes were the most important variables to predict ABC. ABC was also related to the speed of post-fire canopy recovery through which TSLF acts indirectly upon ABC. Finally, we compared remote sensing based aboveground biomass estimates with our inventory based estimates to provide insights on improving their accuracy. The results indicated again that abundances of canopy cover density classes of surficial deposits, and TSLF may serve as ancillary variables for improving substantially the accuracy of remotely sensed biomass estimates. The study results have shown: 1) the importance of lengthening the historical records of fire records to provide a better perspective of the actual changes of fire regime; 2) the importance of incorporating post-fire canopy recovery information together with ABC yield curves in carbon budget models at a spatial scale related to fire disturbances; 3) the importance of adding disturbance history and vegetation recovery trends with remote sensing reflectance data to improve accuracy for biomass mapping