Rôle de la perturbation par le vent dans les forêts tropicales via un modèle dynamique de végétation et l'observation satellitaire

Les perturbations naturelles ont une influence importante sur la structure, la composition et le fonctionnement des forêts tropicales et un rôle dans la régulation des cycles biogéochimiques. La fréquence et l'intensité des perturbations naturelles sont modifiés par les changements climatiques : une meilleure connaissance de leur mécanisme d'action est nécessaire pour prédire les conséquences de cette modification. La modélisation permet d'évaluer le rôle de chacun des processus écologiques et leur lien avec les facteurs environnementaux. Les outils de la télédétection nous informent sur la structure et le fonctionnement des forêts à large échelle, et peuvent être utiles à la calibration et la validation des modèles de végétation. Dans cette thèse, j'ai employé ces deux approches pour examiner comment les forêts tropicales sont façonnées par les perturbations naturelles, notamment le vent, qui est un facteur majeur de perturbation dans de nombreuses régions tropicales. Dans un premier temps, j'ai évalué la transférabilité d'un modèle individu-centré et spatialement explicite via un test de sensibilité et la calibration des paramètres globaux. Le modèle prédit correctement la structure de la forêt sur deux sites contrastés, et sa réponse est cohérente avec les variations du forçage climatique. La calibration d'un petit nombre de paramètres clés a été nécessaire, dont notamment celui qui contrôle la mortalité. Pour étudier la sensibilité du modèle à la mortalité, j'ai mis en œuvre un module de dégâts de vents fondé sur les principes biophysiques et couplé avec la vitesse de vent, afin de modéliser les réponses de la forêt aux évènements de vent extrême. Avec l'augmentation du niveau de perturbation, la hauteur de la canopée diminue de manière constante mais la biomasse montre une réponse non-linéaire. L'intensité du vent a un fort impact sur la hauteur de la canopée et la biomasse, mais pas la fréquence des évènements de vent extrême. Finalement, j'ai testé si les données radar des satellites Sentinel-1 pourraient servir à détecter les trouées dues aux perturbations naturelles en Guyane française. Les données Sentinel-1 détectent plus de trouées naturelles au-dessus de 0.2 ha que les données satellitaires optiques, et elles présentent un patron spatial cohérent avec les images optiques. Le niveau de perturbation ne varie pas en fonction de l'altitude. Nous avons trouvé plus de perturbations pendant les saisons sèches, ce qui pourrait être dû à la réponse tardive des précipitations plutôt qu'à la réponse directe de la sècheresse. En conclusion, cette thèse démontre que l'intégration entre la modélisation et la télédétection éclairent les effets des perturbations naturelles sur les forêts tropicales. Les résultats qui en découlent peuvent servir à étudier d'autres types de perturbations et leurs interactions sur une large échelle.Natural disturbances have an important influence on the structure, composition and functioning of tropical forests and a role in the regulation of biogeochemical cycles. The frequency and intensity of natural disturbances are modified by climate change: a better knowledge of their mechanism of action is necessary to predict the consequences of this modification. Modeling allows us to evaluate the role of each of the ecological processes and their link with environmental factors. Remote sensing tools inform us about the structure and functioning of forests at large scales, and can be useful for the calibration and validation of vegetation models. In this thesis, I employed both approaches to examine how tropical forests are shaped by natural disturbances, particularly wind, which is a major disturbance factor in many tropical regions. First, I evaluated the transferability of a spatially explicit, individual-based model via sensitivity testing and calibration of global parameters. The model correctly predicts forest structure at two contrasting sites, and its response is consistent with variations in climate forcing. Calibration of a small number of key parameters was required, including the parameter controlling mortality and crown allometry. To investigate the sensitivity of the model to mortality, I implemented a wind damage module based on biophysical principles and coupled with wind speed to model forest responses to extreme wind events. With increasing disturbance level, canopy height decreased steadily but biomass showed a non-linear response. Wind intensity had a strong impact on canopy height and biomass, but not the frequency of extreme wind events. Finally, I tested whether radar data from Sentinel-1 satellites could be used to detect gaps due to natural disturbances in French Guiana. The Sentinel-1 data detected more natural gaps above 0.2 ha than the optical satellite data, and they showed a spatial pattern consistent with the optical images. The level of disturbance did not vary with altitude. We found more disturbance during dry seasons, which could be due to the delayed response of precipitation rather than the direct response of drought. In conclusion, this thesis demonstrates that the integration between modeling and remote sensing sheds light on the effects of natural disturbances on tropical forests. The resulting results can be used to study other types of disturbances and their interactions on a large scale

Burned area detection and mapping using Sentinel-1 backscatter coefficient and thermal anomalies

This paper presents a burned area mapping algorithm based on change detection of Sentinel-1 backscatter data guided by thermal anomalies. The algorithm self-adapts to the local scattering conditions and it is robust to variations of input data availability. The algorithm applies the Reed-Xiaoli detector (RXD) to distinguish anomalous changes of the backscatter coefficient. Such changes are linked to fire events, which are derived from thermal anomalies (hotspots) acquired during the detection period by the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Visible Infrared Imaging Radiometer Suite (VIIRS) sensors. Land cover maps were used to account for changing backscatter behaviour as the RXD is class dependent. A machine learning classifier (random forests) was used to detect burned areas where hotspots were not available. Burned area perimeters derived from optical images (Landsat-8 and Sentinel-2) were used to validate the algorithm results. The validation dataset covers 21 million hectares in 18 locations that represent the main biomes affected by fires, from boreal forests to tropical and sub-tropical forests and savannas. A mean Dice coefficient (DC) over all studied locations of 0.59±0.06 (±confidence interval, 95%) was obtained. Mean omission (OE) and commission errors (CE) were 0.43±0.08 and 0.37±0.06, respectively. Comparing results with the MODIS based MCD64A1 Version 6, our detections are quite promising, improving on average DC by 0.13 and reducing OE and CE by 0.12 and 0.06, respectively.European Space AgencyMinisterio de Educación, Cultura y Deport

Predicting forest cover in distinct ecosystems: the potential of multi-source sentinel-1 and -2 data fusion

The fusion of microwave and optical data sets is expected to provide great potential for the derivation of forest cover around the globe. As Sentinel-1 and Sentinel-2 are now both operating in twin mode, they can provide an unprecedented data source to build dense spatial and temporal high-resolution time series across a variety of wavelengths. This study investigates (i) the ability of the individual sensors and (ii) their joint potential to delineate forest cover for study sites in two highly varied landscapes located in Germany (temperate dense mixed forests) and South Africa (open savanna woody vegetation and forest plantations). We used multi-temporal Sentinel-1 and single time steps of Sentinel-2 data in combination to derive accurate forest/non-forest (FNF) information via machine-learning classifiers. The forest classification accuracies were 90.9% and 93.2% for South Africa and Thuringia, respectively, estimated while using autocorrelation corrected spatial cross-validation (CV) for the fused data set. Sentinel-1 only classifications provided the lowest overall accuracy of 87.5%, while Sentinel-2 based classifications led to higher accuracies of 91.9%. Sentinel-2 short-wave infrared (SWIR) channels, biophysical parameters (Leaf Area Index (LAI), and Fraction of Absorbed Photosynthetically Active Radiation (FAPAR)) and the lower spectrum of the Sentinel-1 synthetic aperture radar (SAR) time series were found to be most distinctive in the detection of forest cover. In contrast to homogenous forests sites, Sentinel-1 time series information improved forest cover predictions in open savanna-like environments with heterogeneous regional features. The presented approach proved to be robust and it displayed the benefit of fusing optical and SAR data at high spatial resolution

Continuous Forest Monitoring Using Cumulative Sums of Sentinel-1 Timeseries

Forest degradation is recognized as a major environmental threat on a global scale. The recent rise in natural and anthropogenic destruction of forested ecosystems highlights the need for developing new, rapid, and accurate remote sensing monitoring systems, which capture forested land transformations. In spite of the great technological advances made in airborne and spaceborne sensors over the past decades, current Earth observation (EO) change detection methods still need to overcome numerous limitations. Optical sensors have been commonly used for detecting land use and land cover changes (LULCC), however, the requirement of certain technical and environmental conditions (e.g., sunlight, not cloud-coverage) restrict their use. More recently, synthetic aperture radar (SAR)-based change detection approaches have been used to overcome these technical limitations, but they commonly rely on static detection approaches (e.g., pre and post disturbance scenario comparison) that are slow to monitor change. In this context, this paper presents a novel approach for mapping forest structural changes in a continuous and near-real-time manner using dense Sentinel-1 image time-series. Our cumulative sum−spatial mean corrected (CUSU-SMC) algorithm approach is based on cumulative sum statistical analysis, which allows the continuous monitoring of radar signal variations, derived from forest structural change. Taking advantage of the high data availability offered by the Sentinel-1 (S-1) C-band constellation, we used an S-1 ground range detected (GRD) dual (VV, VH) polarization timeseries, formed by a total of 84 images, to monitor clear-cutting operations carried out in a Scottish forest during 2019. The analysis showed a user’s accuracy of 82% for the (conservative) detection approach. The use of a post-processing neighbor filter increased the detection performance to a user’s accuracy of 86% with an overall accuracy of 77% for areas of a minimum extent of 0.4ha. To further validate the detection performance of the method, the CUSU-SMC change detector was tested against commonly-used pairwise change detection approaches for the same period. These results emphasize the capabilities of dense SAR time-series for environmental monitoring and provide a useful tool for optimizing national forest inventories

Monitoring tropical peat related settlement using ISBAS InSAR, Kuala Lumpur International Airport (KLIA)

Rapid population growth in South-East Asia has placed immense pressure upon lowland regions both to supply food and employment and space for residential, commercial and infrastructure development. This pressure has led to sites on tropical peatland previously considered unsuitable for development to be revisited. One such site, the KLIA2 terminal and runway, Kuala Lumpur International Airport which opened in May 2014 at a cost of 3.6 billion MYR has been beset by well documented subsidence problems. Coverage of the tropics by the Sentinel-1 satellite constellation presents an opportunity to monitor the ongoing subsidence at KLIA 2, identify potential knowledge gaps and help inform more sustainable infrastructure development in tropical peatland regions. Our results show that the ISBAS InSAR method produces reproducible ground deformation maps which can clearly identify the patterns of deformation across both urban infrastructure and adjacent rural plantations and tropical peat swamp. This is particularly well defined around the terminal building at KLIA-2 where different ground preparation and foundation design have resulted in stable terminal buildings and subsidence of surrounding pavement. Deformation is greatest in the runway area where alternate bands of uplift and subsidence presumably accompany the greatest loads associated with landing aircraft. In contrast, areas where peat replacement was the primary form of ground preparation, ground motion is stable, however this comes at high economic and environmental cost

Earth Observations for Addressing Global Challenges

"Earth Observations for Addressing Global Challenges" presents the results of cutting-edge research related to innovative techniques and approaches based on satellite remote sensing data, the acquisition of earth observations, and their applications in the contemporary practice of sustainable development. Addressing the urgent tasks of adaptation to climate change is one of the biggest global challenges for humanity. As His Excellency António Guterres, Secretary-General of the United Nations, said, "Climate change is the defining issue of our time—and we are at a defining moment. We face a direct existential threat." For many years, scientists from around the world have been conducting research on earth observations collecting vital data about the state of the earth environment. Evidence of the rapidly changing climate is alarming: according to the World Meteorological Organization, the past two decades included 18 of the warmest years since 1850, when records began. Thus, Group on Earth Observations (GEO) has launched initiatives across multiple societal benefit areas (agriculture, biodiversity, climate, disasters, ecosystems, energy, health, water, and weather), such as the Global Forest Observations Initiative, the GEO Carbon and GHG Initiative, the GEO Biodiversity Observation Network, and the GEO Blue Planet, among others. The results of research that addressed strategic priorities of these important initiatives are presented in the monograph

The SAR Handbook: Comprehensive Methodologies for Forest Monitoring and Biomass Estimation

This Synthetic Aperture Radar (SAR) handbook of applied methods for forest monitoring and biomass estimation has been developed by SERVIR in collaboration with SilvaCarbon to address pressing needs in the development of operational forest monitoring services. Despite the existence of SAR technology with all-weather capability for over 30 years, the applied use of this technology for operational purposes has proven difficult. This handbook seeks to provide understandable, easy-to-assimilate technical material to remote sensing specialists that may not have expertise on SAR but are interested in leveraging SAR technology in the forestry sector

Mesoscale mapping of sediment source hotspots for dam sediment management in data-sparse semi-arid catchments

Land degradation and water availability in semi-arid regions are interdependent challenges for management that are influenced by climatic and anthropogenic changes. Erosion and high sediment loads in rivers cause reservoir siltation and decrease storage capacity, which pose risk on water security for citizens, agriculture, and industry. In regions where resources for management are limited, identifying spatial-temporal variability of sediment sources is crucial to decrease siltation. Despite widespread availability of rigorous methods, approaches simplifying spatial and temporal variability of erosion are often inappropriately applied to very data sparse semi-arid regions. In this work, we review existing approaches for mapping erosional hotspots, and provide an example of spatial-temporal mapping approach in two case study regions. The barriers limiting data availability and their effects on erosion mapping methods, their validation, and resulting prioritization of leverage management areas are discussed.BMBF, 02WGR1421A-I, GROW - Verbundprojekt SaWaM: Saisonales Wasserressourcen-Management in Trockenregionen: Praxistransfer regionalisierter globaler Informationen, Teilprojekt 1DFG, 414044773, Open Access Publizieren 2019 - 2020 / Technische Universität Berli