Deep learning for Chilean native flora classification: a comparative analysis

The limited availability of information on Chilean native flora has resulted in a lack of knowledge among the general public, and the classification of these plants poses challenges without extensive expertise. This study evaluates the performance of several Deep Learning (DL) models, namely InceptionV3, VGG19, ResNet152, and MobileNetV2, in classifying images representing Chilean native flora. The models are pre-trained on Imagenet. A dataset containing 500 images for each of the 10 classes of native flowers in Chile was curated, resulting in a total of 5000 images. The DL models were applied to this dataset, and their performance was compared based on accuracy and other relevant metrics. The findings highlight the potential of DL models to accurately classify images of Chilean native flora. The results contribute to enhancing the understanding of these plant species and fostering awareness among the general public. Further improvements and applications of DL in ecology and biodiversity research are discussed

Coral Colony-Scale Rugosity Metrics and Applications for Assessing Temporal Trends in the Structural Complexity of Coral Reefs.

Globally, coral reefs are experiencing reductions in structural complexity, primarily due to a loss of key reef building taxa. Monitoring these changes is difficult due to the time-consuming nature of in-situ measurements and lack of data concerning coral genus-specific contributions to reef structure. This research aimed to develop a new technique that uses coral colony level data to quantify reef rugosity (a 3-dimensional measure of reef structure) from three sources of coral survey data: 2D video imagery, line intercept data and UAV imagery. A database of coral colony rugosity data, comparing coral colony planar and contour length for 40 coral genera, 14 morphotypes and 9 abiotic reef substrates, was created using measurements from the Great Barrier Reef and Natural History Museum. Mean genus rugosity was identified as a key trait related to coral life history strategy. Linear regression analyses (y = mx) revealed statistically significant (p < 0.05) relationships between coral colony size and rugosity for every coral genus, morphotype and substrate. The gradient governing these relationships was unique for each coral taxa, ranging from mean = 1.23, for (encrusting) Acanthastrea, to m = 3.84, for (vase-shape) Merulina. These gradients were used as conversion factors to calculate reef rugosity from linear distances measured in video transects of both artificial reefs, used as a control test, and in-situ natural coral reefs, using Kinovea software. This calculated, ‘virtual’ rugosity had a strong, positive relationship with in-situ microscale rugosity (r2 = 0.96) measured from the control transects, but not with that measured at the meso-scale in natural, highly heterogeneous reef environments (r2 < 0.2). This showed that the technique can provide accurate rugosity information when considered at the coral colony level. The conversion factors were also applied to historic line intercept data from the Seychelles, where temporal changes in calculated rugosity were consistent with changes in coral cover between 2008 and 2017. Finally, on application to 2,283 corals digitised from UAV imagery of the Maldives, the conversion factors enabled calculation of rugosity for three 100 m2 reef areas and prediction of how this rugosity will decrease during two future scenarios of coral reef degradation and community change. The study highlights that the application of genera-specific coral rugosity data to both new and existing coral reef survey datasets could be a valuable tool for monitoring reef structural complexity over large spatial scales

Land Surface Monitoring Based on Satellite Imagery

This book focuses attention on significant novel approaches developed to monitor land surface by exploiting satellite data in the infrared and visible ranges. Unlike in situ measurements, satellite data provide global coverage and higher temporal resolution, with very accurate retrievals of land parameters. This is fundamental in the study of climate change and global warming. The authors offer an overview of different methodologies to retrieve land surface parameters— evapotranspiration, emissivity contrast and water deficit indices, land subsidence, leaf area index, vegetation height, and crop coefficient—all of which play a significant role in the study of land cover, land use, monitoring of vegetation and soil water stress, as well as early warning and detection of forest fires and drought

Analyse comparative de l'utilisation de l'apprentissage profond sur des images satellitaires

L'analyse d'images satellites est un domaine de la géomatique permettant de nombreuses observations par rapport à la terre. Une étape importante de toute observation est d'identifier le contenu de l'image. Cette étape est normalement effectuée à la main, ce qui coûte temps et argent. Avec l'avènement des réseaux de neurones profonds, des GPUs à forte capacité de calculs et du nombre croissant de données satellitaires annotées, les algorithmes apprenants sont désormais les outils les plus prometteurs pour l'analyse automatique d'images satellitaires. Ce mémoire présente une étude préliminaire de l'application des réseaux à convolution sur des images satellites, ainsi que deux nouvelles méthodes devant permettre d'entraîner des réseaux de neurones a l'aide de données satellitaires pauvrement annotées. Pour cela, on a utilisé deux bases de données de l'international society for photogrammetry and remote sensing comprenant 40 images étiquetées de six classes. Les deux atouts majeurs de ces bases de données sont la grande variété des canaux composant leurs images, ainsi que les lieux différents (et donc contextes) où ces images ont été acquises. Par la suite, nous présenterons des résultats empiriques à plusieurs questions d'ordre pratique en lien avec les performances attendues des réseaux de neurones profonds appliqués à l'imagerie satellitaire. Vers la fin du rapport, nous présenterons deux techniques permettant de combiner plusieurs ensembles de données, et ce, grâce à des étiquettes de classes hiérarchiques

El Hierro Island Global Geopark

This open access book explores El Hierro Island, which is geologically the youngest of the Canary Islands (Spain). Having registered its latest volcanic eruption in 2011-2012, it is an oceanic subtropical island with low population pressure and a largely unchanged natural landscape. Accordingly, a great geodiversity of volcanic morphologies and erosion processes has been preserved. In addition, half of the land is protected as a Biosphere Reserve and as a UNESCO Global Geopark, and the island is pursuing energy self-sufficiency. Local tourism is a sustainable activity, as the main attractions are either diving or hiking through the island’s various volcanic landscapes. Covering these and other aspects, and using accessible language, the book will appeal to scientists specialized in geotourism, active leisure entrepreneurs, and members of the general public interested in volcanic geoheritage and geotourism

El Hierro Island Global Geopark

This open access book explores El Hierro Island, which is geologically the youngest of the Canary Islands (Spain). Having registered its latest volcanic eruption in 2011-2012, it is an oceanic subtropical island with low population pressure and a largely unchanged natural landscape. Accordingly, a great geodiversity of volcanic morphologies and erosion processes has been preserved. In addition, half of the land is protected as a Biosphere Reserve and as a UNESCO Global Geopark, and the island is pursuing energy self-sufficiency. Local tourism is a sustainable activity, as the main attractions are either diving or hiking through the island’s various volcanic landscapes. Covering these and other aspects, and using accessible language, the book will appeal to scientists specialized in geotourism, active leisure entrepreneurs, and members of the general public interested in volcanic geoheritage and geotourism

Nomos: a comparative political sociology of contemporary national border barriers

Since 2001, there are more than 50 national border barriers around the globe — proposed, under construction, or finished. My dissertation considers this new infrastructure inside larger questions of sovereignty, governance, immigration, and security in the “borderless” age of globalization. To approach this work I used an epistemological framework of border thinking, a “third space” hermeneutics that locates the border as a central place to theorize the complex geopolitical and postcolonial relationships. I conducted two case studies of this fortress infrastructure, one along the U.S.-Mexico border and another along the Costa Rican border with Nicaragua, considering how new border walls are material manifestations of inchoate sovereignty, occupying claims in the borderlands — one of the latest frontier zones of global capital. Broadly, this project calls for us to consider the global proliferation of national border walls and fences in a way that invokes collective action against the persisting operative logic of race/culture thinking that underpins securitization as both a form of governance and an ideology. It situates the urgency of this intellectual work inside the expanding sovereign jurisdictions of capital and opens up new sets of questions about how national border barriers are integral structures inside the changing ideo-political frameworks of war, sovereignty, and governance in the age of the drone

Saint Sebastian and the Garden

Gary Dwyer uses his photographs to investigate our relationship with nature by looking at what we think is a GARDEN.https://digitalcommons.calpoly.edu/books_fac/1003/thumbnail.jp