Discrete anisotropic radiative transfer (DART 5) for modeling airborne and satellite spectroradiometer and LIDAR acquisitions of natural and urban landscapes

International audienceSatellite and airborne optical sensors are increasingly used by scientists, and policy makers, and managers for studying and managing forests, agriculture crops, and urban areas. Their data acquired with given instrumental specifications (spectral resolution, viewing direction, sensor field-of-view, etc.) and for a specific experimental configuration (surface and atmosphere conditions, sun direction, etc.) are commonly translated into qualitative and quantitative Earth surface parameters. However, atmosphere properties and Earth surface 3D architecture often confound their interpretation. Radiative transfer models capable of simulating the Earth and atmosphere complexity are, therefore, ideal tools for linking remotely sensed data to the surface parameters. Still, many existing models are oversimplifying the Earth-atmosphere system interactions and their parameterization of sensor specifications is often neglected or poorly considered. The Discrete Anisotropic Radiative Transfer (DART) model is one of the most comprehensive physically based 3D models simulating the Earth-atmosphere radiation interaction from visible to thermal infrared wavelengths. It has been developed since 1992. It models optical signals at the entrance of imaging radiometers and laser scanners on board of satellites and airplanes, as well as the 3D radiative budget, of urban and natural landscapes for any experimental configuration and instrumental specification. It is freely distributed for research and teaching activities. This paper presents DART physical bases and its latest functionality for simulating imaging spectroscopy of natural and urban landscapes with atmosphere, including the perspective projection of airborne acquisitions and LIght Detection And Ranging (LIDAR) waveform and photon counting signals

Simulation de données et fusion du spectromètre d'imagerie et du système multi-capteurs LiDAR à l'aide d'un modèle dard

International audienceMulti-sensor systems are increasingly demanding in recent remote sensing (RS) applications. Combination of LiDAR and imaging spectrometers is an emerging technique used by several recent airborne systems. The combined data provide both functional and structural information, which makes this technique a unique tool for understanding and management of the Earth's ecosystems. The rapid development of this technique demands the simulation and validation of the combined data. In this paper, we introduce a new method to simulate data fusion of multi-sensor system which combined LiDAR and imaging spectrometer, with any experimental, instrumental, and geometrical configurations of systems. This method is implemented in the latest release of discrete anisotropic radiative transfer (DART) model

Monitoring and characterization of landscape closure using frequential texture information derived from VHRS satellite images in Mediterranean region

International audienceMediterranean ecosystems constitute one of the world's biodiversity hotspots, and are currently going through a major biome crisis. Open habitats such as meadows, grasslands and wetlands are of high ecological interest, yet they are undergoing profound changes in structure by progressive closing of the landscape, caused by changes in traditional management practices. Thus, it is imperative to be able to monitor and quantify changes in landscape closure, including rates of change, structural and functional changes at different scales in the landscape. The recent arrival of new satellite sensors, such as Spot 6/7, and the improved accessibility of remote sensing data through the Theia Land Data Center, provides the opportunity to develop new methods and services derived from these images. We assessed and measure the degree of openness of natural open habitats from Mediterranean landscapes in Southern France. The cover fraction of three types of land covers (grass, shrubs, and trees) was determined using a textural approach on Spot 6/7 satellite sensor images. Different methods were investigated. A statistical approach on the grey level of the images with the use of Haralick indices permitted to segregate open habitats from close ones. But this method was limited in the case of “mixed habitats” and showed poor results when trying to establish a closure gradient. Better results were found using a method based on a frequential analysis of the images. Both closure gradient and “coarseness” of the habitats have been assessed with more accuracy than the previous methods. These results show that it is possible to monitor landscape closure at broad scale, using optical remote sensing data. This information constitute an important first step towards finer characterisation of habitats that can be performed with complementary sources of remote sensing data with appropriate temporal or spectral characteristics

How to capture the mediterranean forested landscape complexity using remote-sensing tools

International audienceThe Mediterranean forests have been used for millennia and are organized according to a heterogeneous and complex landscape particularly beneficial to biological diversity. The forests have been degraded by overgrazing and exploitation for firewood, but also as a result of fires. Such forest areas may become open, secondary forests with several understories, but if not properly managed they may turn into varied types of high or low matorral (dry shrubland) or in some areas to heathlands that could be degraded into sparsely vegetated areas. As a result, there is an urgent need for monitoring tools to inform changes in this valuable ecosystem to support public policies to protect a sustainable management of these biodiversity hotspots. In the framework of the Essential Biodiversity Variables (EBV) developed by the GEO-BON, six classes of variables have been defined to cover different key indicators, including ecosystem structure, which is intimately linked to the fauna and flora richness. Remotely-sensed earth observation (RS) has become essential to provide a rapid, repeated and synoptic access of these EBVs (i.e., RS-enabled EBVs): the increasing availability of open access satellite data provides enhanced possibilities to monitor this natural landscape under increasing anthropic pressures. Mediterranean ecosystem could be monitor with various types of sensors, providing information on key indicators like structure, function and composition at frequent revisit times and high to very high spatial resolution. The support of innovative descriptors involving tools such as light sensors embedded on drones, spectro-radiometers or terrestrial Lidar are also needed

Landscape structure estimation using Fourier-based ordination of high resolution airborne optical image

International audienceLandscape heterogeneity is a key factor for understanding ecosystems function and is often associated to high biodiversity. Yet, its characterization for biodiversity monitoring still remains challenging, especially for very heterogeneous landscapes mosaics. In this work, we study the potential of Fourier-based Textural Ordination (FOTO) applied on high resolution airborne optical images to provide synthetic information about landscape fragmentation of Mediterranean heterogeneous landscapes. The ability of texture indices resulting from FOTO combined with vegetation indices to predict landscape metrics was also tested using non linear SVM regression. Our research showed that FOTO methods had great potential to finely characterize different vegetation strata organization at large scale with only few synthetic indices. Best regression results were obtained for ligneous and ground strata fragmentation with coefficient of determination greater than 0.7

Reconstruction de la trajectoire d'un scanner laser aéroporté à partir des données

International audienceMulti-echo airborne laser scanner (ALS) has shown increasing utility for forestry applications in the two past decades. Among the numerous algorithms developed to process ALS data on forest environments some require to know actual sensor trajectory and deduced angles of incidence. However, sensor trajectory is not part of the ALS standard LAS file format and is often not delivered with point clouds. Scan angle is usually specified with a one byte precision or not given at all.This paper presents a method for the reconstruction of the sensor trajectory from a multi-echo ALS point cloud. It is based on the intersection of multi-echo pulses and was tested on three data sets acquired over a deciduous, a tropical and a mountainous forest, respectively. It allows sensor location estimate and scan angle estimate with less than 25 cm and 2-10-2° error

On the use of shortwave infrared for tree species discrimination in tropical semideciduous forest

ISPRS Geospatial Week 2015, La Grande Motte, FRA, 28-/09/2015 - 03/10/2015International audienceTree species mapping in tropical forests provides valuable insights for forest managers. Keystone species can be located for collection of seeds for forest restoration, reducing fieldwork costs. However, mapping of tree species in tropical forests using remote sensing data is a challenge due to high floristic and spectral diversity. Little is known about the use of different spectral regions as most of studies performed so far used visible/near-infrared (390-1000 nm) features. In this paper we show the contribution of shortwave infrared (SWIR, 1045-2395 nm) for tree species discrimination in a tropical semideciduous forest. Using high-resolution hyperspectral data we also simulated WorldView-3 (WV-3) multispectral bands for classification purposes. Three machine learning methods were tested to discriminate species at the pixel-level: Linear Discriminant Analysis (LDA), Support Vector Machines with Linear (L-SVM) and Radial Basis Function (RBF-SVM) kernels, and Random Forest (RF). Experiments were performed using all and selected features from the VNIR individually and combined with SWIR. Feature selection was applied to evaluate the effects of dimensionality reduction and identify potential wavelengths that may optimize species discrimination. Using VNIR hyperspectral bands, RBF-SVM achieved the highest average accuracy (77.4%). Inclusion of the SWIR increased accuracy to 85% with LDA. The same pattern was also observed when WV-3 simulated channels were used to classify the species. The VNIR bands provided and accuracy of 64.2% for LDA, which was increased to 79.8 % using the new SWIR bands that are operationally available in this platform. Results show that incorporating SWIR bands increased significantly average accuracy for both the hyperspectral data and WorldView-3 simulated bands

Dynamique des mangroves à partir de la télédétection optique à très haute résolution spatiale

International audienceAssessing the role of tropical forests in biogeochemical cycles is one of the greatest scientific challenges of the century. The impact of climate change could increase the mortality of tropical forests and decrease their ability to store atmospheric carbon dioxide. Efforts to model the forest dynamics in these regions are hindered by the lack of ground measurements and the spatial-temporal overlap of function management processes. Some of the models developed attempt to show the environment’s influence on forest dynamics, particularly by studying the capacity for canopy deformation (plasticity of the crown), as explained in Purves et al. The models allow us to better understand the development of intertree competition, the capacity of species to adapt to changes and how the forest functions as a whole.L'évaluation du rôle des forêts tropicales dans les cycles biogéochimiques est l'un des plus grands défis scientifiques du siècle. L'impact du changement climatique pourrait accroître la mortalité des forêts tropicales et diminuer leur capacité à stocker le dioxyde de carbone atmosphérique. Les efforts visant à modéliser la dynamique forestière dans ces régions sont entravés par l'absence de mesures au sol et le chevauchement spatio-temporel des processus de gestion des fonctions. Certains des modèles développés tentent de montrer l'influence de l'environnement sur la dynamique des forêts, en particulier en étudiant la capacité de déformation de la canopée (plasticité de la couronne), comme expliqué dans Purves et al. Les modèles nous permettent de mieux comprendre le développement de la compétition entre individus, la capacité des espèces à s'adapter aux changements et comment la forêt fonctionne dans son ensemble

Fusion de données LiDAR et hyperspectrales pour la gestion forestière - une revue

International audienceAccording to the Intergovernmental Panel on Climate Change (IPCC), forests represent an essential source of all carbon stocks in vegetation for maintaining life conditions of many organisms in the terrestrial biosphere. The utilization of strategies for forest characterization and monitoring, plays an imperative role to develop a proper sustainable management. Current research in the field is focused on sensor potentiality and data processing. Recent advances in remote sensing afford valuable information to describe forests at tree level. On the one hand, hyperspectral images contain meaningful reflectance attributes of plants or spectral traits. On the other hand, LiDAR data offers alternatives for analyzing structural properties of canopy. A convenient selection of fusion methods provide better and more robust estimation of the variable of interest. This work presents a literature review for the integration of hyperspectral images and LiDAR data by considering applications related to forestry monitoring. Although different authors propose a variety of taxonomies for data fusion, we classified our reviewed methods according to three levels of fusion based on data processing: Low level or observation level, medium level or feature level, and high level or decision level. Fusion at observation level preserves most of the original information from both modalities by handling data at the same spatial dimension. Canopy Height Model (CHM) is the most used two-dimensional representation of LiDAR point cloud for the registration with hyperspectral images. Fusion at feature level seeks to complement information by exploiting the original data. The most relevant features extracted from hyperspectral or LiDAR data are statistical, morphological, structural, vegetation indexes, textural, among others. Some of these feature descriptors are stacked to be fused at higher level, or these are normalized to be integrated through methods of dimension reduction or feature selection. Fusion at decision level is directly associated to the forestry application and implies tasks of thresholding, segmentation, classification, or regression analysis. This review examines a relationship between the three levels of fusion and the methods used in each considered approach. The most important applications listed in this work are oriented to individual tree crown delineation, tree specie classification, landcovermaps, aboveground biomass estimation, and biophysical parameters