A statistical algorithm for estimating chlorophyll concentration in the New Caledonian lagoon

Spatial and temporal dynamics of phytoplankton biomass and water turbidity can provide crucial information about the function, health and vulnerability of lagoon ecosystems (coral reefs, sea grasses, etc.). A statistical algorithm is proposed to estimate chlorophyll-a concentration ([chl-a]) in optically complex waters of the New Caledonian lagoon from MODIS-derived remote-sensing reflectance (R-rs). The algorithm is developed via supervised learning on match-ups gathered from 2002 to 2010. The best performance is obtained by combining two models, selected according to the ratio of R-rs in spectral bands centered on 488 and 555 nm: a log-linear model for low [chl-a] (AFLC) and a support vector machine (SVM) model or a classic model (OC3) for high [chl-a]. The log-linear model is developed based on SVM regression analysis. This approach outperforms the classical OC3 approach, especially in shallow waters, with a root mean squared error 30% lower. The proposed algorithm enables more accurate assessments of [chl-a] and its variability in this typical oligo- to meso-trophic tropical lagoon, from shallow coastal waters and nearby reefs to deeper waters and in the open ocean

La recuperation de memoire dans les machines non-deterministes

SIGLECNRS T Bordereau / INIST-CNRS - Institut de l'Information Scientifique et TechniqueFRFranc

Automated, web-based environment for daily fire risk assessment in New Caledonia

International audienceIn New Caledonia, the increasing number, frequency, and extent of fires represent both a danger to human life and a threat to ecosystems conservation in an area considered to be a sanctuary of biodiversity. A four-year study was carried out in the framework of the INC research project to better understand why fires start and to improve fire prevention. Based on satellite observations, a model was built to calculate the risk of fire ignition, together with a Bayesian network to link data on social, environmental, and climatological risk factors. The model and environmental, geological, and topographical data can be used to assess the impacts of fire on biodiversity and erosion. The purpose of this paper is to present a tool designed to produce on-the-fly maps showing fire risk obtained. This web-based tool implements the model built during the research project and provides fire risk maps every day thanks to an automated recovery of climate data. The user only has to select the date of interest in order that the tool manages all the process of maps creation and display. Open Geospatial Consortium (OGC) is an international organization in which governments, trade research, and non-profit structures collaborate to implement open standards related to services and geospatial content, as well as GIS data processing and sharing. The presented tool has been made with some standards specified by OGC (Geography Markup Language, Web Map Service), allowing it to be interoperable with other systems that implements OGC standards. An online tool that exploits live results of the model and automated filling of a database enables identification of high fire risk sites. It then facilitates the task of land and environmental management by combining geographic data on location of water resources and roads that can be used to reach the potentially dangerous sites of fires

Ocean remote sensing and monitoring from space

We propose a statistical algorithm to assess chlorophyll-a concentration ([chl-a]) using remote sensing reflectance (Rrs) derived from MODerate Resolution Imaging Spectroradiometer (MODIS) data. This algorithm is a combination of two models: one for low [chl-a] (oligotrophic waters) and one for high [chl-a]. A satellite pixel is classified as low or high [chla] according to the Rrs ratio (488 and 555 nm channels). If a pixel is considered as a low [chl-a] pixel, a log-linear model is applied; otherwise, a more sophisticated model (Support Vector Machine) is applied. The log-linear model was developed thanks to supervised learning on Rrs and [chl-a] data from SeaBASS and more than 15 campaigns accomplished from 2002 to 2010 around New Caledonia. Several models to assess high [chl-a] were also tested with statistical methods. This novel approach outperforms the standard reflectance ratio approach. Compared with algorithms such as the current NASA OC3, Root Mean Square Error is 30% lower in New Caledonian waters

A statistical algorithm for estimating chlorophyll concentration from MODIS data

International audienc

A statistical algorithm for estimating chlorophyll concentration from MODIS data

International audienc

A spatially explicit integrative model for estimating the risk of wildfire impacts in New-Caledonia

In this study, we present a spatially explicit bayesian model which is able to predict the distribution of potential fire ignition at a grid resolution of over the entire main island of New-Caledonia. This statistical model, when used in conjunction with a mechanistic fire model (FlamMap), is able to estimate the comparative and spatialized risk of fire impact on specific areas for specific issues such as biodiversity or erosion. The input data includes variables related to the physical environment such as the topography, climate, and some geographical indicators relating to human influences such as population density and type of land property. [GRAPHICS] Flowchart computation for the specific cell i. The grey rectangles denote the input data, the trapezoids denote the intermediate indices and the rounded corners rectangles denote the integrated wildfire risk indices. The connection legends indicates the corresponding equations and below certain nodes a drawing describes an example related to the main variables and the associated area used

Multiscale Effects of Collagen Damage in Cortical Bone and Dentin

The denaturation of collagen at the molecular level in bone and dentin can impact their structure and properties, leading to increased brittleness in pathological diseases such as osteogenesis imperfecta, dentinogenesis imperfecta, diabetes, and cancer. This study investigates the relationship between collagen denaturation and the macroscale resistance of bone and dentin. Through heat treatment at 160∘C on bovine bone and human dentin, the effects of collagen denaturation on macroscale flexural strength, scanning electron microscopy, and transmission electron microscopy imaging of micro- and nanostructure were studied. The results show that collagen denaturation decreases the resistance of bone and dentin to fracture, even though collagen denaturation did not impact the mineral organization around and inside collagen fibrils. This is attributable to (1) a reduction in bone and dentin ability to deform (e.g., 40–75% decrease in strain to failure) and to resist fracture (e.g., 83–95% decrease in work to fracture) properties and (2) to a smoother crack path with less crack deflection around microstructural features. Reduction in deformation and toughness not only removed plastic deformation but also drastically decreased elastic deformation and elastic work to fracture in all tissues. However, the elastic modulus was only affected in radial-oriented bone samples where collagen fibrils are oriented perpendicularly to crack opening forces. This study highlights the crucial role of collagen molecule integrity and orientation in bone/dentin deformability and resistance

An environmental management plan in the Vavouto harbor (New Caledonia) with a statistical treatment displayed on dynamic maps

International audienceThis paper presents an environmental monitoring tool to control and share results in real time as part of an environmental management plan (EMP) associated with a major dredging operation in the northern part of the New Caledonian lagoon. The interoperable tool includes results collected by a network of sensors that monitor turbidity and other physicochemical parameters of both the water and sediment