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Temperate forest ecology --- Forest biodiversity --- Temperate forests --- Yellow pines

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Les écosystèmes terrestres dissipent l’énergie incidente sous deux formes principales
qui sont caractéristiques de leur fonctionnement : la chaleur sensible (H) et latente (LE).
La répartition de l’énergie disponible entre celles-ci détermine non seulement l’état
d’équilibre physiologique de tels systèmes mais également leur impact sur les
paramètres physiques de l’atmosphère environnant. Partant de ce constat, ce travail
propose d’investiguer la dynamique inter- et intra-annuelle des deux flux de chaleur
turbulents par l’étude de leurs interactions. Il a pour vocation à la fois d’identifier les
tendances présentes dans les évolutions temporelles des échanges de H et LE tout en les
rattachant aux processus éco-physiologiques sous-jacents qui affectent la distribution de
l’énergie disponible de l’écosystème. Dans cette optique, la transformée en ondelettes
continue, outil d’analyse temps-fréquence dont la prépondérance s’établit
progressivement dans le domaine climatique, a été implémentée. Le jeu de données se
constitue de 14 années de mesures de flux à la demi-heure de chaleur sensible et latente
récoltés par eddy covariance au-dessus d’une jeune hêtraie à Hesse, Nord-Est de la France.
Par le biais de la méthodologie précitée, il a été possible de déceler de nombreux
comportements périodiques à des échelles intermédiaires, comprises entre la journée et
l’année, sur toute l’étendue de la saison de croissance. Qui plus est, les variables
directrices des évolutions de H et LE ont pu être mises en évidence. Parmi celles-ci, le
rayonnement incident possède la corrélation la plus importante avec les deux flux. Par
ailleurs, la déplétion des réserves en eau du sol a été associée à d’importants
mouvements périodiques, aux échelles intermédiaires. Enfin, au départ de l’ensemble de
ces variables, des mécanismes fonctionnels propres aux écosystèmes forestiers tempérés
ont été déduits, illustrant globalement la grande résilience du système étudié. A terme,
ce travail propose d’explorer les perspectives d’analyses à long terme de mesures
micro-climatiques offertes par l’utilisation de la méthode de transformée en ondelettes
continue, qui est présumée devenir un outil de plus en plus prisé à l’aube du changement
climatique planétaire actuel. Terrestrial ecosystems dissipate incident energy in two main forms according to their
own characteristics : sensible (H) and latent (LE) heat. The distribution of available
energy between these forms determines not only the physiological equilibrium state of
such systems but also their impact on the physical parameters of the surrounding
atmosphere. Based on this observation, this study investigates the inter- and intra-annual
dynamics of both mentioned turbulent heat fluxes while taking their interactions into
account. Its purpose is to identify patterns in temporal evolution of H and LE exchanges
while linking them to underlying eco-physiological processes that affect the distribution
of ecosystem’s available energy. Following this perspective, the continuous wavelet
transform, a time-frequency analysis tool whose relevance is gradually being established
in the climate field, has been implemented. The dataset consists of 14 years of half-hourly
fluxes of sensible and latent heat obtained by eddy-covariance over a young beech forest
at Hesse, North-eastern France. By applying the above-mentioned methodology, it was
possible to detect many periodic behaviours at intermediate scales, ranging from days to
years, throughout the growing season. Moreover, the main drivers of H and LE fluxes
were highlighted. Among these, incident radiation has the highest correlation with both
fluxes. In addition, depletion of soil water content has been associated with large
periodic movements at intermediate scales. From these drivers, functional mechanisms
specific to temperate forest ecosystems were deduced, illustrating overall the high
resilience of the studied system. Ultimately, this work explores the opportunities of
long-term analyses of microclimatic measurements offered by the use of the continuous
wavelet transform, which is expected to become an increasingly popular tool at the dawn
of current global climate change.

wavelet coherence --- Sensible heat --- latent heat --- energy partitioning --- eddy covariance --- temperate forest --- time series analysis --- wavelet analysis --- chaleur sensible --- chaleur latente --- répartition énergétique --- eddy covariance --- forêt tempérée --- analyse du signal --- transformée en ondelettes --- cohérence --- Sciences du vivant > Sciences de l'environnement & écologie --- Physique, chimie, mathématiques & sciences de la terre > Mathématiques

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Globally, fire regimes are being altered by changing climatic conditions and land use changes. This has the potential to drive species extinctions and cause ecosystem state changes, with a range of consequences for ecosystem services. Accurate prediction of the risk of forest fires over short timescales (weeks or months) is required for land managers to target suppression resources in order to protect people, property, and infrastructure, as well as fire-sensitive ecosystems. Over longer timescales, prediction of changes in forest fire regimes is required to model the effect of wildfires on the terrestrial carbon cycle and subsequent feedbacks into the climate system.This was the motivation to publish this book, which is focused on quantifying and modelling the risk factors of forest fires. More specifically, the chapters in this book address four topics: (i) the use of fire danger metrics and other approaches to understand variation in wildfire activity; (ii) understanding changes in the flammability of live fuel; (iii) modeling dead fuel moisture content; and (iv) estimations of emission factors.The book will be of broad relevance to scientists and managers working with fire in different forest ecosystems globally.

fire danger rating --- fire management --- fire regime --- fire size --- fire weather --- Portugal --- critical LFMC threshold --- forest/grassland fire --- radiative transfer model --- remote sensing --- southwest China --- acid rain --- aerosol --- biomass burning --- forest fire --- PM2.5 --- direct estimation --- meteorological factor regression --- moisture content --- time lag --- forest fire driving factors --- forest fire occurrence --- random forest --- forest fire management --- China --- Cupressus sempervirens --- fire risk --- fuels --- fuel moisture content --- mass loss calorimeter --- Seiridium cardinale --- vulnerability to wildfires --- disease --- alien pathogen --- allochthonous species --- introduced fungus --- drying tests --- humidity diffusion coefficients --- wildfire --- prescribed burning --- modeling --- drought --- flammability --- fuel moisture --- leaf water potential --- plant traits --- climate change --- MNI --- fire season --- fire behavior --- crown fire --- fire modeling --- senescence --- foliar moisture content --- canopy bulk density --- fire danger --- fire weather patterns --- RCP --- FWI system --- SSR --- occurrence of forest fire --- machine learning --- variable importance --- prediction accuracy --- epicormic resprouter --- eucalyptus --- fire severity --- flammability feedbacks --- temperate forest --- n/a

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The topics of the book cover forest parameter estimation, methods to assess land cover and change, forest disturbances and degradation, and forest soil drought estimations. Airborne laser scanner data, aerial images, as well as data from passive and active sensors of different spatial, spectral and temporal resolutions have been utilized. Parametric and non-parametric methods including machine and deep learning methods have been employed. Uncertainty estimation is a key topic in each study. In total, 15 articles are included, of which one is a review article dealing with methods employed in remote sensing aided greenhouse gas inventories, and one is the Editorial summary presenting a short review of each article.

Research & information: general --- Environmental economics --- forest structure change --- EBLUP --- small area estimation --- multitemporal LiDAR and stand-level estimates --- forest cover --- Sentinel-1 --- Sentinel-2 --- data fusion --- machine-learning --- Germany --- South Africa --- temperate forest --- savanna --- classification --- Sentinel 2 --- land use land cover --- improved k-NN --- logistic regression --- random forest --- support vector machine --- statistical estimator --- IPCC good practice guidelines --- activity data --- emissions factor --- removals factor --- Picea crassifolia Kom --- compatible equation --- nonlinear seemingly unrelated regression --- error-in-variable modeling --- leave-one-out cross-validation --- digital surface model --- digital terrain model --- canopy height model --- constrained neighbor interpolation --- ordinary neighbor interpolation --- point cloud density --- stereo imagery --- remotely sensed LAI --- field measured LAI --- validation --- magnitude --- uncertainty --- temporal dynamics --- state space models --- forest disturbance mapping --- near real-time monitoring --- CUSUM --- NRT monitoring --- deforestation --- degradation --- tropical forest --- tropical peat --- forest type --- deep learning --- FCN8s --- CRFasRNN --- GF2 --- dual-FCN8s --- random forests --- error propagation --- bootstrapping --- Landsat --- LiDAR --- La Rioja --- forest area change --- data assessment --- uncertainty evaluation --- inconsistency --- forest monitoring --- drought --- time series satellite data --- Bowen ratio --- carbon flux --- boreal forest --- windstorm damage --- synthetic aperture radar --- C-band --- genetic algorithm --- multinomial logistic regression --- forest structure change --- EBLUP --- small area estimation --- multitemporal LiDAR and stand-level estimates --- forest cover --- Sentinel-1 --- Sentinel-2 --- data fusion --- machine-learning --- Germany --- South Africa --- temperate forest --- savanna --- classification --- Sentinel 2 --- land use land cover --- improved k-NN --- logistic regression --- random forest --- support vector machine --- statistical estimator --- IPCC good practice guidelines --- activity data --- emissions factor --- removals factor --- Picea crassifolia Kom --- compatible equation --- nonlinear seemingly unrelated regression --- error-in-variable modeling --- leave-one-out cross-validation --- digital surface model --- digital terrain model --- canopy height model --- constrained neighbor interpolation --- ordinary neighbor interpolation --- point cloud density --- stereo imagery --- remotely sensed LAI --- field measured LAI --- validation --- magnitude --- uncertainty --- temporal dynamics --- state space models --- forest disturbance mapping --- near real-time monitoring --- CUSUM --- NRT monitoring --- deforestation --- degradation --- tropical forest --- tropical peat --- forest type --- deep learning --- FCN8s --- CRFasRNN --- GF2 --- dual-FCN8s --- random forests --- error propagation --- bootstrapping --- Landsat --- LiDAR --- La Rioja --- forest area change --- data assessment --- uncertainty evaluation --- inconsistency --- forest monitoring --- drought --- time series satellite data --- Bowen ratio --- carbon flux --- boreal forest --- windstorm damage --- synthetic aperture radar --- C-band --- genetic algorithm --- multinomial logistic regression

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The topics of the book cover forest parameter estimation, methods to assess land cover and change, forest disturbances and degradation, and forest soil drought estimations. Airborne laser scanner data, aerial images, as well as data from passive and active sensors of different spatial, spectral and temporal resolutions have been utilized. Parametric and non-parametric methods including machine and deep learning methods have been employed. Uncertainty estimation is a key topic in each study. In total, 15 articles are included, of which one is a review article dealing with methods employed in remote sensing aided greenhouse gas inventories, and one is the Editorial summary presenting a short review of each article.

forest structure change --- EBLUP --- small area estimation --- multitemporal LiDAR and stand-level estimates --- forest cover --- Sentinel-1 --- Sentinel-2 --- data fusion --- machine-learning --- Germany --- South Africa --- temperate forest --- savanna --- classification --- Sentinel 2 --- land use land cover --- improved k-NN --- logistic regression --- random forest --- support vector machine --- statistical estimator --- IPCC good practice guidelines --- activity data --- emissions factor --- removals factor --- Picea crassifolia Kom --- compatible equation --- nonlinear seemingly unrelated regression --- error-in-variable modeling --- leave-one-out cross-validation --- digital surface model --- digital terrain model --- canopy height model --- constrained neighbor interpolation --- ordinary neighbor interpolation --- point cloud density --- stereo imagery --- remotely sensed LAI --- field measured LAI --- validation --- magnitude --- uncertainty --- temporal dynamics --- state space models --- forest disturbance mapping --- near real-time monitoring --- CUSUM --- NRT monitoring --- deforestation --- degradation --- tropical forest --- tropical peat --- forest type --- deep learning --- FCN8s --- CRFasRNN --- GF2 --- dual-FCN8s --- random forests --- error propagation --- bootstrapping --- Landsat --- LiDAR --- La Rioja --- forest area change --- data assessment --- uncertainty evaluation --- inconsistency --- forest monitoring --- drought --- time series satellite data --- Bowen ratio --- carbon flux --- boreal forest --- windstorm damage --- synthetic aperture radar --- C-band --- genetic algorithm --- multinomial logistic regression --- n/a