Snow cover persistence as a useful predictor of alpine plant distributions

Aim: We examine whether the addition of snow cover persistence in plant species distribution models (SDMs) improves their predictive power. We investigate the link between species’ ecology and SDM improvements by the addition of various snow cover persistence predictors. Location: Western Swiss Alps. Taxon: 206 alpine flowering plants (Angiospermes). Methods: We produced three maps of landsat satellite-based snow cover persistence indices over an entire mountain region, one of them using an online open access platform allowing quick and easy replication and used them as a predictor in plant SDMs alongside commonly used predictors. We tested whether this improved the predictive performance of plant SDMs. Results: All three snow cover persistence indices improved the overall SDM predictive accuracy, but the overall improvement was potentially limited by their correlation with other climatic predictors. Alpine plant species known for their dependence on snow benefited more from the additional snow information. Main conclusions: Snow cover persistence should be used for predicting at least the distribution of alpine, snow related plant species. Given that adding snow cover improves SDMs and that snow duration decreases as climate warms, future predictions of alpine plant distributions should account for both snow predictor and associated snow change scenarios

From white to green : Snow cover loss and increased vegetation productivity in the European Alps

Mountains are hotspots of biodiversity and ecosystem services, but they are warming about twice as fast as the global average. Climate change may reduce alpine snow cover and increase vegetation productivity, as in the Arctic. Here, we demonstrate that 77% of the European Alps above the tree line experienced greening (productivity gain) andPeer reviewe

Madagascar's fire regimes challenge global assumptions about landscape degradation

Narratives of landscape degradation are often linked to unsustainable fire use by local communities. Madagascar is a case in point: the island is considered globally exceptional, with its remarkable endemic biodiversity viewed as threatened by unsustainable anthropogenic fire. Yet, fire regimes on Madagascar have not been empirically characterised or globally contextualised. Here, we contribute a comparative approach to determining relationships between regional fire regimes and global patterns and trends, applied to Madagascar using MODIS remote sensing data (2003–2019). Rather than a global exception, we show that Madagascar's fire regimes are similar to 88% of tropical burned area with shared climate and vegetation characteristics, and can be considered a microcosm of most tropical fire regimes. From 2003–2019, landscape-scale fire declined across tropical grassy biomes (17%–44% excluding Madagascar), and on Madagascar at a relatively fast rate (36%–46%). Thus, high tree loss anomalies on the island (1.25–4.77× the tropical average) were not explained by any general expansion of landscape-scale fire in grassy biomes. Rather, tree loss anomalies centred in forests, and could not be explained by landscape-scale fire escaping from savannas into forests. Unexpectedly, the highest tree loss anomalies on Madagascar (4.77×) occurred in environments without landscape-scale fire, where the role of small-scale fires (<21 h [0.21 km2]) is unknown. While landscape-scale fire declined across tropical grassy biomes, trends in tropical forests reflected important differences among regions, indicating a need to better understand regional variation in the anthropogenic drivers of forest loss and fire risk. Our new understanding of Madagascar's fire regimes offers two lessons with global implications: first, landscape-scale fire is declining across tropical grassy biomes and does not explain high tree loss anomalies on Madagascar. Second, landscape-scale fire is not uniformly associated with tropical forest loss, indicating a need for socio-ecological context in framing new narratives of fire and ecosystem degradation

Multiple point geostatistical approaches to spectrally enhance satellite imagery

The information content of Earth surface satellite images are getting richer and richer. In the long process from gray images on silver film to multispectral digital imagery, lots of different types of image were acquired. Currently images allow doing high quality classification, change detection, etc. The combined use of imagery of different generations is a challenge for long-term studies. The usual solution for a long-term study is to decimate spectral information of satellite imagery to a common level. Instead, in this thesis I propose to use geostatistics and in particular multiple-points statistics (MPS), tools originally developed to simulate subsurface processes using an analogue image (training image), to improve the usefulness of poor satellite imagery by artificially harmonizing their spectral resolution. Due to the computational and parametrization challenges related to the use of existing MPS approaches for spectral enhancement, a new method was developed. Quantile Sampling (QS) is a robust and efficient solution to realize MPS simulations. Furthermore, QS was designed to be easily set, with few and independent parameters. QS was developed, with the primary constraint to handle continuous values in a particular efficient manner. The spectral enhancement can be divided into two distinct tasks. First spectral disaggregation, such as converting a gray image into a color image, is addressed with traditional MPS algorithms. Second, the spectral extrapolation, such as determining near infrared from visible color image, is explored using a new framework: Narrow Distribution Selection (NDS) dedicated to this type of task. Built on top of the QS ideas, NDS provides high quality simulations by providing high probability simulations. Finally, a new and simple calibration framework is presented. Even if QS reduces the sensitivity to algorithm parameterization and simplifies it significantly, calibration is still required. Here, a method to automatically determine an optimal verbatim-free calibration is presented. This method relies on the complete analysis of the training image. Furthermore, the method provides a calibration adapted to each step of the simulation. -- Les images satellites de la surface de la terrestre sont de plus en plus riches. Au cours de l’évolution qui s’étale de l’imagerie argentique monochromatique jusqu’aux dernières avancées en imagerie numérique multispectrale, de nombreux types d’images différentes ont été acquis. Les images actuelles permettent une classification de hautefidélité, la détection de changements, etc. L'utilisation d'images de différentes générations est un réel défi pour les études qui considère les évolutions sur le long terme. La solution habituelle utilisée consiste à réduire les informations spectrales des images satellitaires à un dénominateur commun. Dans cette thèse je propose d'utiliser la géostatistique et en particulier les statistiques multipoints (MPS), qui ont été développées à l'origine pour simuler des processus souterrains à l'aide d'images analogues (images d’entrainement), afin d'améliorer l'utilité des images satellites pauvres en harmonisant artificiellement leurs résolutions spectrales. Dans le cadre de l’enrichissement spectral, une nouvelle méthode a été développée en raison des lourdeurs de calcul et de paramétrage liées à l'utilisation des approches MPS existantes. Quantile Sampling (QS) est une solution robuste et efficace pour réaliser des simulations MPS. De plus, QS a été conçu pour être configuré facilement, avec peu de paramètres et des paramètres indépendants. QS a été développé avec comme contrainte principale de gérer les variables continues de manière extrêmement efficace. L’enrichissement spectral peut être divisé en deux tâches distinctes. La première tâche, la désagrégation spectrale, telle que la conversion d'une image grise en image couleur, peut être traitée à l'aide des algorithmes MPS traditionnels. La deuxième, l'extrapolation spectrale, telle que la reconstruction du proche infrarouge à partir d'une image en couleur, est explorée à l'aide d’une nouvelle approche NDS (Narrow Distribution Selection) spécialement développée pour ce type de tâche. S'appuyant sur les bases de QS, NDS fournit des simulations de haute qualité, grâce à des simulations de forte probabilité. Enfin, une nouvelle méthode de calibration est présentée. Si QS simplifie le paramétrage et en réduit significativement la sensibilité, une calibration est toujours nécessaire. La méthode présentée permet de déterminer automatiquement une calibration optimale sans verbatim. Cette méthode repose sur l'analyse complète de l'image d’entrainement. De plus, le procédé fournit une calibration évolutive adaptée à chaque étape de la simulation

Open Earth Engine Toolbox, code goodies and extension for Google Earth Engine

&amp;lt;p&amp;gt;The Open Earth Engine Toolbox (OEET) is an innovative and highly effective suite of tools that makes it easier than ever to work with Google Earth Engine. Comprised of two main components - the Open Earth Engine Library (OEEL) and the Open Earth Engine Extension (OEEex) - the OEET is a true game-changer for anyone working in the field of geospatial analysis and data processing.&amp;lt;/p&amp;gt; &amp;lt;p&amp;gt;The OEEL is a set of JavaScript code libraries that provide a wide range of functions and capabilities for working with Earth Engine. From advanced filtering techniques such as the Savitzky-Golay and Otsu algorithms, to powerful visualization tools like north arrows, map scales, mapshots&amp;amp;#8230; the OEEL has everything you need to get the most out of Earth Engine. And with a convenient Python wrapper, it&amp;#039;s easy to integrate the OEEL into your existing workflow, scripts or notebooks.&amp;lt;/p&amp;gt; &amp;lt;p&amp;gt;But the OEEex is where the OEET really shines. This Chrome extension is designed you to work in tandem with the OEEL, unlocking a host of additional features and capabilities. One of the standout features of the OEEex is its ability to all run tasks in a single step. This is particularly useful for those working with large datasets or for those who need to perform the same tasks repeatedly. With the OEEex, you can simply set up your tasks and then let the extension handle the rest, saving you time and effort of clicking on each. The OEEex also offers a range of customization options for the interface. These include the ability to switch to a dark mode, which can be easier on the eyes during long work sessions, as well as the ability to adjust font sizes to suit your personal preference. It allows to utilize Plotly within Earth Engine to get beautiful figure, and much more.&amp;lt;/p&amp;gt; &amp;lt;p&amp;gt;Overall, the OEET is an essential tool for anyone looking to get the most out of Google Earth Engine. Its powerful JavaScript libraries, convenient Python wrapper, and feature-rich Chrome extension make it the go-to choice for geospatial analysis and data processing.&amp;lt;/p&amp;gt;</jats:p