Mapping Natura 2000 Habitat Conservation Status in a Pannonic Salt Steppe with Airborne Laser Scanning

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Développement d’une méthode de cartographie des services écologiques en appui à l’aménagement durable des forêts : application au bassin versant de la rivière Harry, Terre-Neuve, Canada

La cartographie d'un service écologique (SE) est un moyen efficace pour communiquer l'importance des SEs auprès des gestionnaires forestiers et pour démontrer que la forêt est gérée de façon durable. La cartographie des SEs facilite leur intégration dans le processus décisionnel. Ainsi, ce mémoire propose un cadre méthodologique pour cartographier l’apport potentiel d’un SE développé tout d'abord pour un SE de régulation lié à l'eau - le service de contrôle des sédiments (SCS) - pour un bassin versant dominé par la forêt dans l'ouest de Terre-Neuve au Canada (640 km²). Ensuite le cadre est appliqué à un SE culturel - le service de la chasse - afin de tester la reproductibilité de la démarche méthodologique. Notre démarche méthodologique repose sur une approche « de relations causales de variables de substitution (proxies) », moins complexe à mettre en œuvre que les modèles physiques et moins subjective que les méthodes basées sur l'opinion d'experts. Les variables de substitution sont calculées à partir des données spatiales disponibles et agrégées en un indicateur composé qui est utilisé pour classer sur une échelle relative les sous bassins versants. Deux indicateurs composés ont été développés pour le SCS. Le premier utilise des pondérations égales pour chaque indicateur de substitution et pour le second des poids basés sur l'opinion d'experts ont été attribués. L'utilisation d'un indicateur composé (appelé indice) pour la cartographie permet de prendre en compte la nature multidimensionnelle et complexe des SEs. Les variables de substitution représentent les indicateurs de fonction des écosystèmes (IF) nécessaires pour décrire la relation de causalité entre les fonctions écologiques et le SE associé. Une évaluation du cadre de cartographie est réalisée pour le SCS en comparant les échelles relatives du SCS à une classification du rendement en sédiments simulé à l'aide du modèle hydrologique SWAT (Soil Water Assessment Tool). La précision globale varie de 35 à 81% en fonction des périodes de simulation et du système de pondération et présente de meilleurs résultats pour les périodes comportant davantage d'opérations de gestion forestière et pour le système de pondération basé sur les experts. Les résultats de la mise en œuvre du cadre montrent l’apport potentiel du SCS et du service culturel de la chasse à l’échelle des sous-bassins versants et mettent en évidence les sous-bassins versants les plus susceptibles d’être affectés par les opérations de gestion forestière

Integrating Remote Sensing and Machine Learning to Assess Forest Health and Susceptibility to Pest-induced Damage

Spruce budworm (Choristoneura fumiferana; SBW) outbreaks are cyclically occurring phenomena in the northeastern USA and neighboring Canadian provinces. These outbreaks are often of landscape level causing impaired growth and mortality of the host species namely spruce (Picea sp.) and balsam fir (Abies balsamea (L.) Mill.). Acknowledging the recent SBW outbreak in Canadian provinces like Quebec and New Brunswick neighboring the state of Maine, our study devised comprehensive techniques to assess the susceptibility of Maine forests to SBW attack. This study aims to harness the power of remote sensing data and machine learning algorithms to model and map the susceptibility of forest in terms of host species availability and abundance (basal area per hectare; BAPH, and leaf area index; LAI), their maturity and the defense mechanism prevalent. In terms of host species abundance mapping our study explores the integration of satellite remote sensing data to model BAPH and LAI of two economically vital SBW host species, red spruce (Picea rubens Sarg.) and balsam fir, in Maine USA. Combining Sentinel-1 synthetic aperture radar (SAR), Sentinel-2 multispectral, and site variables, we used Random Forest (RF) and Multi-Layer Perceptron (MLP) algorithms for modeling LAI and BAPH. The results demonstrated the superiority of RF over MLP, achieving smaller normalized root mean square error (nRMSE) by 0.01 and 0.06 for LAI and BAPH, respectively. Notably, Sentinel-2 variables, especially the red-edge spectral vegetation indices, played a significant role in both LAI and BAPH estimation, with the minor inclusion of site variables, particularly elevation. In addition, using various satellite remote sensing data such as Sentinel-1 C-band SAR, PALSAR L-band SAR and Sentinel-2 multispectral, along with site variables, the study developed large-scale SBW stand impact types and susceptibility maps for the entire state of Maine. The susceptibility of the forest was assessed based on the availability of SBW host species and their maturity. Integrating machine-learning algorithms, RF and MLP, the best model, utilizing site (elevation and aspect) and Sentinel-2 data achieved an overall accuracy of 83.4% to predict SBW host species. Furthermore, combining the host species data with age data from Land Change Monitoring, Assessment, and Projection (LCMAP) products we could produce the SBW susceptibility map based on stand impact types with an overall accuracy of 88.3%. Moreover, the work builds upon the assessment of susceptibility of SBW host species taking into account the concentration of several canopy traits using remote sensing and site data. The study focused on various foliar traits affecting insect herbivory, including nutritive such as nitrogen (N), phosphorous (P), potassium (K), and copper (Cu), non-nutritive such as iron (Fe) and calcium (Ca), and defensive parameters such as equivalent water thickness (EWT) and leaf mass per area (LMA). Using Sentinel-2 and site data, we developed trait estimation models using machine-learning algorithms like Random Forest (RF), Extreme Gradient Boosting (XGB), and Support Vector Machine (SVM). The accuracy of the developed model was evaluated based on the normalized root mean square error (nRMSE). Based on the model performances, we selected XGB algorithm to estimate Ca, EWT, Fe, and K whereas Cu, LMA, N, and P were estimated using RF algorithm. Regarding the variables used, almost all the best performing models included Sentinel-2 red-edge indices and depth to water table (DWT) as the most important variables. Ultimately, the study proposed a novel framework connecting the concentrations of foliar traits in SBW host foliage to tree susceptibility to the pest, enabling the assessment of host susceptibility on a landscape level. To sum up, this study highlights the advantages and effectiveness of integrating satellite remote sensing data for enhanced pest management, providing valuable insights into tree attributes and susceptibility to spruce budworm outbreaks in Northeast USA. The findings offer essential tools for forest stakeholders to improve management strategies and mitigate potential forthcoming SBW outbreaks in the region

Effective design and use of indicators for marine conservation

The design, selection and use of indicators for large-scale conservation policy has been of great interest since the Convention on Biological Diversity (CBD) committed to a significant reduction in the rate of biodiversity loss by 2010. Following the introduction of the 2020 Aichi Targets, there was an increase, not only in demand for numbers of indicators, but the requirements that they are expected to meet. The complexities of social-ecological systems and the inevitable trade-offs that exist within them mean understanding and validating indicator responses are critical if they are to play a role in active management. In this thesis, I look critically at uncertainties around how indicators are constructed and used, through the lens of marine science and conservation. I start the thesis by exploring the different types of uncertainty found when using composite indicators and from reviewing the literature, suggest possible methods of dealing with them. I find that structural uncertainties of indicators are rarely acknowledged. As a case study of application of composite indicators, I developed an Ocean Health Index assessment for the Arctic Ocean, demonstrating how a structured framework can be of great use for taking a data-driven approach to assessing social-ecological systems in large, data-poor regions. I show the Arctic is sustainably delivering a range of benefits to people, but with room for improvement in all areas, particularly tourism, fisheries, and protected places. Successful management of biological resources and short-term positive impacts on biodiversity in response to climate change underlie these high goal scores. I then explore how two biodiversity indicators (Living Planet Index and Norway Nature Index) can be better interpreted and validated using an end-to-end ecosystem model, Atlantis, in the Nordic and Barents Seas. By simulating different fishing scenarios, I evaluated the extent to which the model-based testing approach gave insights into indicator behaviour; while the LPI is able to distinguish clearly between three different fishing scenarios, the NNI is only able to distinguish the most heavily fished scenario from the other two. I discuss how this approach is useful for indicator testing and to advance integration of large-scale biodiversity indicators with goal-setting and decision making at the system scale. I then use the model to explore how different indicators of biodiversity from across fisheries and conservation respond to management interventions in Norway in the face of climate change. I find that despite having the same intentions, fisheries and conservation biodiversity indicators respond differently to each other under the same scenarios, due to how they are constructed. This means that without proper validation, indicators can potentially give different pictures of the same system to different interest groups, meaning greater integration and understanding of conservation and fisheries management objectives is necessary. Finally, I reflect on the findings of my thesis in light of the CBD Post-2020 Framework. I discuss several core areas where the process could be revised to improve biodiversity outcomes. This includes formulating a robust theory of change to give the framework a clear conceptual basis and explicitly articulate the causal assumptions about the relationship between actions and outcomes. I do not focus on what targets should look like, but instead seek proactive, solutions-oriented approaches that can help ‘bend the curve’ for biodiversity. This thesis highlights the uncertainties and challenges associated with large-scale indicator design and use and demonstrates how countries can take steps to reduce these. Greater consideration of the systems within which indicators are based can lead to better validation and ultimately better decision making.Open Acces