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1. Submonthly Assessment of Temperate Forest Clear-Cuts in Mainland France

Author

Stephane Mermoz, Juan Doblas Prieto, Milena Planells, David Morin, Thierry Koleck, Florian Mouret, Alexandre Bouvet, Thuy Le Toan, David Sheeren, Yousra Hamrouni, Thierry Belouard, Eric Paillassa, Marion Carme, Michel Chartier, Simon Martel, and Jean-Baptiste Feret

Subjects
Fast clear-cuts detection, France, Sentinel-1 SAR image time series, temperate forests, Ocean engineering, TC1501-1800, Geophysics. Cosmic physics, QC801-809
Abstract

Remote sensing satellites allow large-scale and fast detections of forest loss. Operational forest loss detection systems have been mainly developed over tropical forests; however, it is increasingly important to have access to accurate and up-to-date information on temperate forests. In this article, we adapted a Sentinel-1-based near real-time tropical forest loss detection method, based on the radar change ratio, to detect French temperate forests clear-cuts. Using ancillary data, annual and submonthly clear-cuts were assessed for broadleaf and conifer forests, for various tree species, over public and private forests. Using 967 validation plots, the maps exhibited recall and precision of 80.9% and 99.4%, respectively. The clear-cuts area shows remarkable stability over time from 2020. We found seven times more clear-cuts in private forests than in public forests, although the surface area of private forests is only three times that of public forests. It was also demonstrated that only 1.6% out of 4 530 dieback reference plots, and 6.2% of maps of forest bark beetle attacks, were confused with clear-cuts before clear-cuts actually occurred, which makes our maps complementary with forest dieback maps. Collectively, the findings of this study could have significant implications for the implementation of a radar-satellite-based system designed for the real-time detection of large-scale clear-cuts in European temperate forests.

Published
2024
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2. Télédétection des infrastructures agro-écologiques : retour sur le projet Tel-IAE

Author

Christophe SAUSSE and David SHEEREN

Subjects
télédétection, analyse spatiale, accompagnement des acteurs, biodiversité, agriculture, cartographie, Environmental technology. Sanitary engineering, TD1-1066, Environmental sciences, GE1-350
Abstract

Cartographier les infrastructures agroécologiques est une étape importante pour évaluer la qualité des paysages agricoles et prédire l’impact des aménagements. La télédétection spatiale présente d’importants atouts pour atteindre cet objectif à coût raisonnable et sur une surface importante. Terminé il y a six ans, le projet Tel-IAE « Télédétection des infrastructures agroécologiques » a permis d’évaluer des méthodes existantes dans des cas variés et d’en développer de nouvelles. Que s’est-il passé depuis ? Après un rapide retour d’expérience du projet, les auteurs nous livrent ici quelques pistes pour le futur tant du point de vue des usages de la télédétection que de celui des progrès techniques.

Published
2022
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3. Deep Learning for Automatic Colorization of Legacy Grayscale Aerial Photographs

Author

Quentin Poterek, Pierre-Alexis Herrault, Grzegorz Skupinski, and David Sheeren

Subjects
Colorization, deep learning, generative adversarial network (GAN), grayscale imagery, historical aerial photograph, remote sensing, Ocean engineering, TC1501-1800, Geophysics. Cosmic physics, QC801-809
Abstract

Legacy grayscale aerial photographs represent one of the main available sources for studying the past state of the environment and its relationship to the present. However, these photographs lack spectral information thereby hindering their use in current remote sensing approaches that rely on spectral data for characterizing surfaces. This article proposes a conditional generative adversarial network, a deep learning model, to enrich legacy photographs by predicting color channels for an input grayscale image. The technique was used to colorize two orthophotographs (taken in 1956 and 1978) covering the entire Eurométropole de Strasbourg. To assess the model's performances, two strategies were proposed: first, colorized photographs were evaluated with metrics such as peak signal-to-noise ratio (PSNR), and structural similarity index (SSIM); second, random forest classifications were performed to extract land cover classes from grayscale and colorized photographs, respectively. The results revealed strong performances, with PSNR = 25.56 ± 2.20 and SSIM = 0.93 ± 0.06 indicating that the model successfully learned the mapping between grayscale and color photographs over a large territory. Moreover, land cover classifications performed on colorized data showed significant improvements over grayscale photographs, respectively, +6% and +17% for 1956 and 1978. Finally, the plausibility of outputs images was evaluated visually. We conclude that deep learning models are powerful tools for improving radiometric properties of old aerial grayscale photographs and land cover mapping. We also argue that the proposed approach could serve as a basis for further developments aiming to promote the use of aerial photographs archives for landscapes reconstruction.

Published
2020
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4. Sentinel-2 Poplar Index for Operational Mapping of Poplar Plantations over Large Areas

Author

Yousra Hamrouni, Eric Paillassa, Véronique Chéret, Claude Monteil, and David Sheeren

Subjects
populus, spectral index, feature selection, tree species, image classification, countrywide, Science
Abstract

Poplar (Populus spp.) is a fast-growing tree planted to meet the growing global demand for wood products. In France, the country with the largest area planted with poplar in Europe, accurate and up-to-date maps of its spatial distribution are not available at the national scale. This makes it difficult to estimate the extent and location of the poplar resource and calls for the development of a robust and timely stable approach for mapping large areas in order to ensure efficient monitoring. In this study, we investigate the potential of the Sentinel-2 time series to map the diversity of poplar plantations at the French countrywide scale. By comparing multiple configurations of spectral features based on spectral bands and indices over two years (2017 and 2018), we identify the optimal spectral regions with their respective time periods to distinguish poplar plantations from other deciduous species. We also define a novel poplar detection index (PI) with four variants that combine the best discriminative spectral bands. The results highlight the relevance of SWIR followed by red edge regions, mainly in the growing season, to accurately detect poplar plantations, reflecting the sensitivity of poplar trees to water content throughout their phenological cycle. The best performances with stable results were obtained with the PI2 poplar index combining the B5, B11, and B12 spectral bands. The PI2 index was validated over two years with an average producer’s accuracy of 92% in 2017 and 95% in 2018. This new index was used to produce the national map of poplar plantations in 2018. This study provides an operational approach for monitoring the poplar resource over large areas for forest managers.

Published
2022
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5. Individual Tree Crown Delineation Method Based on Multi-Criteria Graph Using Geometric and Spectral Information: Application to Several Temperate Forest Sites

Author

Matthieu Deluzet, Thierry Erudel, Xavier Briottet, David Sheeren, and Sophie Fabre

Subjects
individual tree crown, delineation, LiDAR, passive optical image, temperate forest, coniferous, Science
Abstract

Individual tree crown (ITC) delineation in temperate forests is challenging owing to the presence of broadleaved species with overlapping crowns. Mixed coniferous/deciduous forests with characteristics that differ with the type of tree thus require a flexible method of delineation. The ITC delineation method based on the multi-criteria graph (MCG-Tree) addresses this problem in temperate monospecific or mixed forests by combining geometric and spectral information. The method was used to segment trees in three temperate forest sites with different characteristics (tree types, species distribution, planted or natural forest). Compared with a state-of-the-art watershed segmentation approach, our method increased delineation performance by up to 25%. Our results showed that the main geometric criterion to improve delineation quality is related to the crown radius (performance improvement around 8%). Coniferous/deciduous classification automatically adapts the MCG-Tree criteria to the type of tree. Promising results are then obtained to improve delineation performance for mixed forests.

Published
2022
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6. The influence of spatial and temporal discontinuities of forest habitats on the current presence of flightless saproxylic beetles.

Author

Eugénie Cateau, Pierre-Alexis Herrault, David Sheeren, Sylvie Ladet, and Hervé Brustel

Subjects
Medicine, Science
Abstract

Flightless saproxylic beetles were selected in order to study the impact of temporal and spatial discontinuity of forests. They were chosen because: (1) they are unable to fly, making them dispersal-limited species, (2) they have a saproxylic diet, which means they are closely linked to the forest, and (3), they have rarely been studied. Forest temporal continuity was expected to be the main factor explaining the presence of these species, modulated by the past and present amount of forest in the surrounding landscape. Twenty-seven forests, distributed into three zones, were sampled in southwestern France. Flightless saproxylic beetles were surveyed using a Winkler extractor and a Berlese funnel. Their presence/absence were modelled using generalised linear mixed models, with zone variable as random effect. Two species showed significant zone effect and were only or more present in the zone with the highest present forest amount in a 0.5 km radius. In the model that converged, the only selected variable was the past amount of forest in the landscape. The size of the forest, the presence of dead wood and the forest temporal continuity were not included in this model. The importance of the amount of forest in the landscape supports the hypothesis that dispersal-limited species are affected by landscape characteristics. This study demonstrates an important link between the presence of Dienerella clathrata and the amount of forest in the past, which led to an indicator species analysis being performed.

Published
2018
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7. Statistical Stability and Spatial Instability in Mapping Forest Tree Species by Comparing 9 Years of Satellite Image Time Series

Author

Nicolas Karasiak, Jean-François Dejoux, Mathieu Fauvel, Jérôme Willm, Claude Monteil, and David Sheeren

Subjects
tree species, forest, biodiversity, time series, spatial autocorrelation, cross-validation, accuracy, Science
Abstract

Mapping forest composition using multiseasonal optical time series remains a challenge. Highly contrasted results are reported from one study to another suggesting that drivers of classification errors are still under-explored. We evaluated the performances of single-year Formosat-2 time series to discriminate tree species in temperate forests in France and investigated how predictions vary statistically and spatially across multiple years. Our objective was to better estimate the impact of spatial autocorrelation in the validation data on measurement accuracy and to understand which drivers in the time series are responsible for classification errors. The experiments were based on 10 Formosat-2 image time series irregularly acquired during the seasonal vegetation cycle from 2006 to 2014. Due to lot of clouds in the year 2006, an alternative 2006 time series using only cloud-free images has been added. Thirteen tree species were classified in each single-year dataset based on the Support Vector Machine (SVM) algorithm. The performances were assessed using a spatial leave-one-out cross validation (SLOO-CV) strategy, thereby guaranteeing full independence of the validation samples, and compared with standard non-spatial leave-one-out cross-validation (LOO-CV). The results show relatively close statistical performances from one year to the next despite the differences between the annual time series. Good agreements between years were observed in monospecific tree plantations of broadleaf species versus high disparity in other forests composed of different species. A strong positive bias in the accuracy assessment (up to 0.4 of Overall Accuracy (OA)) was also found when spatial dependence in the validation data was not removed. Using the SLOO-CV approach, the average OA values per year ranged from 0.48 for 2006 to 0.60 for 2013, which satisfactorily represents the spatial instability of species prediction between years.

Published
2019
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8. A Generic Framework for Combining Multiple Segmentations in Geographic Object-Based Image Analysis

Author

Sébastien Lefèvre, David Sheeren, and Onur Tasar

Subjects
GEOBIA, segmentation fusion, segmentation evaluation, consensus, remote sensing, Geography (General), G1-922
Abstract

The Geographic Object-Based Image Analysis (GEOBIA) paradigm relies strongly on the segmentation concept, i.e., partitioning of an image into regions or objects that are then further analyzed. Segmentation is a critical step, for which a wide range of methods, parameters and input data are available. To reduce the sensitivity of the GEOBIA process to the segmentation step, here we consider that a set of segmentation maps can be derived from remote sensing data. Inspired by the ensemble paradigm that combines multiple weak classifiers to build a strong one, we propose a novel framework for combining multiple segmentation maps. The combination leads to a fine-grained partition of segments (super-pixels) that is built by intersecting individual input partitions, and each segment is assigned a segmentation confidence score that relates directly to the local consensus between the different segmentation maps. Furthermore, each input segmentation can be assigned some local or global quality score based on expert assessment or automatic analysis. These scores are then taken into account when computing the confidence map that results from the combination of the segmentation processes. This means the process is less affected by incorrect segmentation inputs either at the local scale of a region, or at the global scale of a map. In contrast to related works, the proposed framework is fully generic and does not rely on specific input data to drive the combination process. We assess its relevance through experiments conducted on ISPRS 2D Semantic Labeling. Results show that the confidence map provides valuable information that can be produced when combining segmentations, and fusion at the object level is competitive w.r.t. fusion at the pixel or decision level.

Published
2019
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10. Spatial and Temporal Dependency of NDVI Satellite Imagery in Predicting Bird Diversity over France

Author

Sébastien Bonthoux, Solenne Lefèvre, Pierre-Alexis Herrault, and David Sheeren

Subjects
MODIS, seasonal NDVI, bird species richness, dynamic habitat index, breeding bird, species distribution model, habitat productivity, habitat heterogeneity, macroecology, Science
Abstract

Continuous-based predictors of habitat characteristics derived from satellite imagery are increasingly used in species distribution models (SDM). This is especially the case of Normalized Difference Vegetation Index (NDVI) which provides estimates of vegetation productivity and heterogeneity. However, when NDVI predictors are incorporated into SDM, synchrony between biological observations and image acquisition must be questionned. Due to seasonal variations of NDVI during the year, landscape patterns of habitats are revealed differently from one date to another leading to variations in models’ performance. In this paper, we investigated the influence of acquisition time period of NDVI to explain and predict bird community patterns over France. We examined if the NDVI acquisition period that best fit the bird data depends on the dominant land cover context. We also compared models based on single time period of NDVI with one model built from the Dynamic Habitat Index (DHI) components which summarize variations in vegetation phenology throughout the year from the fraction of radiation absorbed by the canopy (fPAR). Bird species richness was calculated as response variable for 759 plots of 4 km2 from the French Breeding Bird Survey. Bird specialists and generalists to habitat were considered. NDVI and DHI predictors were both derived from MODIS products. For NDVI, five time periods in 2010 were compared, from late winter to begin of autumn. A climate predictor was also used and Generalized Additive Models were fitted to explain and predict bird species richness. Results showed that NDVI-based proxies of dominant habitat identity and spatial heterogeneity explain more bird community patterns than DHI-based proxies of annual productivity and seasonnality. We also found that models’ performance was both time and context-dependent, varying according to the bird groups. In general, best time period of NDVI did not match with the acquisition period of bird data because in case of synchrony, differences in habitats are less pronounced. These findings suggest that the most powerful approach to estimate bird community patterns is the simplest one. It only requires NDVI predictors from a single appropriate time period, in addition to climate, which makes the approach very operational.

Published
2018
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11. Object-Based Classification of Grasslands from High Resolution Satellite Image Time Series Using Gaussian Mean Map Kernels

Author

Mailys Lopes, Mathieu Fauvel, Stéphane Girard, and David Sheeren

Subjects
supervised classification, SVM, Gaussian mean map kernels, kernel methods, object analysis, grasslands, Science
Abstract

This paper deals with the classification of grasslands using high resolution satellite image time series. Grasslands considered in this work are semi-natural elements in fragmented landscapes, i.e., they are heterogeneous and small elements. The first contribution of this study is to account for grassland heterogeneity while working at the object level by modeling its pixels distributions by a Gaussian distribution. To measure the similarity between two grasslands, a new kernel is proposed as a second contribution: the α -Gaussian mean kernel. It allows one to weight the influence of the covariance matrix when comparing two Gaussian distributions. This kernel is introduced in support vector machines for the supervised classification of grasslands from southwest France. A dense intra-annual multispectral time series of the Formosat-2 satellite is used for the classification of grasslands’ management practices, while an inter-annual NDVI time series of Formosat-2 is used for old and young grasslands’ discrimination. Results are compared to other existing pixel- and object-based approaches in terms of classification accuracy and processing time. The proposed method is shown to be a good compromise between processing speed and classification accuracy. It can adapt to the classification constraints, and it encompasses several similarity measures known in the literature. It is appropriate for the classification of small and heterogeneous objects such as grasslands.

Published
2017
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12. TOXI-CITY: an agent-based model for exploring the effects of risk awareness and spatial configuration on the survival rate in the case of industrial accidents

Author

Paul Salze, Elise Beck, Johnny Douvinet, Marion Amalric, Emmanuel Bonnet, Éric Daudé, Françoise Duraffour, and David Sheeren

Subjects
simulation, urban morphology, technological risk, agent-based model, spatial behavior, Geography (General), G1-922
Abstract

Industrial accidents are a major risk in many countries. Despite this observation, effective preventive action is not being developed and studies of the behaviors of vulnerable populations remain under-explored. According to safety instructions, residents must take shelter and remain indoors but other behaviors may be observed such as: running away from the affected area, panicking, or following people around. In order to assess the attitudes of people and the effects of local contingencies that could interfere with official advice in the event of a toxic cloud, a specific agent-based model called TOXI-CITY has been developed. The aim of this explorative model is to determine whether a minimum number of well-informed agents (i.e. those who follow the emergency protocol) can save a maximum number of uninformed and impressionable ones. Simulations indicate that the initial number of agents and the spatial configuration of the grid strongly influence final survival rates. A non-linear pattern emerged: survival rates increase when well-informed agent rates are low (below 30%) while increasing the percentage of informed agents (from 70%) seems to improve only slightly the chances of survival.

Published
2014
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13. Télédétection des éléments semi-naturels : utilisation des données Pléiades pour la détection des haies

Author

Mathieu Fauvel, Carole Planque, David Sheeren, and Mauro Dalla Mura

Subjects
Pléiades, haies, détection, orientation locale., Instruments and machines, QA71-90, Applied optics. Photonics, TA1501-1820, Cellular telephone services industry. Wireless telephone industry, HE9713-9715
Abstract

La détection des haies à partir d'images Pléiades est abordée dans cet article. Un système de détection en deux étapes est proposé. Il est basé sur l'utilisation conjointe de l'information spatiale et de l'information spectrale. Tout d'abord, les éléments ligneux sont détectés en utilisant l'information spectrale. Puis, l'orientation locale de chaque pixel est calculée à l'aide de filtres directionnels. Les expériences menées sur une image multispectrale Pléiades acquise en septembre 2013 montrent une bonne performance de l'approche proposée (> 90% de pixels bien classés) et permet d'envisager une détection du réseau de haies sur de grandes étendues.

Published
2014
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14. Tree Species Classification in Temperate Forests Using Formosat-2 Satellite Image Time Series

Author

David Sheeren, Mathieu Fauvel, Veliborka Josipović, Maïlys Lopes, Carole Planque, Jérôme Willm, and Jean-François Dejoux

Subjects
tree species, forest, time series, classification, smoothing, Whittaker, phenology, biodiversity, Science
Abstract

Mapping forest composition is a major concern for forest management, biodiversity assessment and for understanding the potential impacts of climate change on tree species distribution. In this study, the suitability of a dense high spatial resolution multispectral Formosat-2 satellite image time-series (SITS) to discriminate tree species in temperate forests is investigated. Based on a 17-date SITS acquired across one year, thirteen major tree species (8 broadleaves and 5 conifers) are classified in a study area of southwest France. The performance of parametric (GMM) and nonparametric (k-NN, RF, SVM) methods are compared at three class hierarchy levels for different versions of the SITS: (i) a smoothed noise-free version based on the Whittaker smoother; (ii) a non-smoothed cloudy version including all the dates; (iii) a non-smoothed noise-free version including only 14 dates. Noise refers to pixels contaminated by clouds and cloud shadows. The results of the 108 distinct classifications show a very high suitability of the SITS to identify the forest tree species based on phenological differences (average κ = 0 . 93 estimated by cross-validation based on 1235 field-collected plots). SVM is found to be the best classifier with very close results from the other classifiers. No clear benefit of removing noise by smoothing can be observed. Classification accuracy is even improved using the non-smoothed cloudy version of the SITS compared to the 14 cloud-free image time series. However conclusions of the results need to be considered with caution because of possible overfitting. Disagreements also appear between the maps produced by the classifiers for complex mixed forests, suggesting a higher classification uncertainty in these contexts. Our findings suggest that time-series data can be a good alternative to hyperspectral data for mapping forest types. It also demonstrates the potential contribution of the recently launched Sentinel-2 satellite for studying forest ecosystems.

Published
2016
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19. HedgeTools : un outil d’analyse spatiale dédié à l’évaluation de la multifonctionnalité des haies

Author

Gabriel Marquès, Lewis Villierme, Jean-Baptiste Boissonnat, Guillaume Guébin, Marc Lang, Claude Monteil, and David Sheeren

Subjects
GIS, spatial analysis, hedgerow, viewpoint, multiple representation, topology, Mathematical geography. Cartography, GA1-1776
Abstract

Hedgerows plays key roles in agricultural landscapes but their automatic characterization by spatial analysis is complex. In this article, we describe the main functionalities of an open source tool – HedgeTools – which makes it possible to calculate a variety of indicators helping to assess the multifunctionality of hedges. It allows for creating the geometry of objects, cutting them according to various criterias, and extracting their characteristics at different levels of aggregation. HedgeTools aims to facilitate the management and preservation of hedges by making it possible to evaluate their condition and their functions in landscapes, with prospects for improving and extending its functionalities.

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22. BIODIVERSITY – A new space mission to monitor Earth ecosystems at fine scale

Author

Xavier BRIOTTET, Touria BAJJOUK, Malik CHAMI, Christophe DELACOURT, Jean-Baptiste FERET, Stephane JACQUEMOUD, Audrey MINGHELLI, David SHEEREN, Christiane WEBER, Sophie FABRE, Karine ADELINE, Emmanuelle VAUDOUR, Sandra LUQUE, Yannick DEVILLE, Kamel SOUDANI, and Charles VERPOOTER

Subjects
Electrical and Electronic Engineering, Computer Science Applications
Abstract

Imaging spectroscopy has demonstrated its interest in characterizing the biochemical, biophysical and structural properties of vegetation, natural and agricultural soils, as well as artificial surfaces. Following the Hyperion mission, new space missions have emerged (PRISMA, EnMap), or are under study (CHIME, SBG). However, one of their main limitations lies in their spatial resolution that induces a large number of mixed pixels reducing their potential for discrimination for very heterogeneous areas. The BIODIVERSITY mission aims to complement these space missions with better GSD acquisitions (typically 8-10 m) with a 5-day revisit on targeted reference sites with identified and well-located characteristics. It will thus make it possible, in particular, to answer two scientific issues that will design the instrument. The first issue concerns the spatial and temporal distribution of vegetation traits in species assemblages; these traits are associated with the resilience of terrestrial ecosystems, anthropogenic influences, and the biodiversity of ecosystems in terms of species composition and assemblages. The second issue relates to improving our knowledge of coastal areas and inland waters in terms of biodiversity, water quality and bathymetry, in order to assess the impact of human activity on their ecosytems. The scientific challenges as well as the user requirements for such a mission are presented for each application.

Published
2022
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31. Spatial dependence between training and test sets: another pitfall of classification accuracy assessment in remote sensing

Author

N. Karasiak, Claude Monteil, Jean-François Dejoux, David Sheeren, Dynamiques et écologie des paysages agriforestiers (DYNAFOR), École nationale supérieure agronomique de Toulouse [ENSAT]-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Centre d'études spatiales de la biosphère (CESBIO), Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS), and French Ministry of Higher Education and Research (University of Toulouse)

Subjects
Contextual image classification, Pixel, Property (programming), Computer science, Overfitting, Cross-validation, 02 engineering and technology, Remote sensing, Python (programming language), Accuracy assessment, Artificial Intelligence, Sample size determination, 020204 information systems, 0202 electrical engineering, electronic engineering, information engineering, [INFO]Computer Science [cs], 020201 artificial intelligence & image processing, Spatial dependence, computer, Spatial analysis, Spatial autocorrelation, Software, Independence (probability theory), computer.programming_language
Abstract

International audience; Spatial autocorrelation is inherent to remotely sensed data. Nearby pixels are more similar than distant ones. This property can help to improve the classification performance, by adding spatial or contextual features into the model. However, it can also lead to overestimation of generalisation capabilities, if the spatial dependence between training and test sets is ignored. In this paper, we review existing approaches that deal with spatial autocorrelation for image classification in remote sensing and demonstrate the importance of bias in accuracy metrics when spatial independence between the training and test sets is not respected. We compare three spatial and non-spatial cross-validation strategies at pixel and object levels and study how performances vary at different sample sizes. Experiments based on Sentinel-2 data for mapping two simple forest classes show that spatial leave-one-out cross-validation is the better strategy to provide unbiased estimates of predictive error. Its performance metrics are consistent with the real quality of the resulting map contrary to traditional non-spatial cross-validation that overestimates accuracy. This highlight the need to change practices in classification accuracy assessment. To encourage it we developped Museo ToolBox, an open-source python library that makes spatial cross-validation possible.

Published
2021
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35. Sentinel-HR Phase 0 Report

Author

Julien Michel, Olivier Hagolle, Jean-Marc Delvit, Martin Thierry, Lebegue Laurent, David Laubier, Jean Philippe Mallet, Legenne Jérome, Joël Michaud, Vinasco-Salinas Juan, Christelle Iliopoulos, Sébastien Le Corre, Pauline Dusseux, Andreas Max Kääb, Hervé Yesou, Jeremie Mouginot, Stephanie Battiston, Stephen Clandillon, Bergsma, Erwin W. J., Etienne Berthier, Antoine Lucas, Jean-François Dejoux, Gérard Dedieu, Anne Puissant, Sylvain Ferrant, David Sheeren, Antoine Lefevre, Jean-Philippe Cantou, Jean-François Crétaux, Rafael Almar, Camille Lelong, Pierre Maurel, Jean-François Faure, Jean Nabucet, Thomas Corpetti, Pierre-Alexis Herrault, Centre d'études spatiales de la biosphère (CESBIO), Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Centre National d'Études Spatiales [Toulouse] (CNES), Centre National d'Etudes Spatiales - Direction Des Lanceurs. (CNES), Ecole et Observatoire des Sciences de la Terre (EOST), Université de Strasbourg (UNISTRA)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS), Université Toulouse Capitole (UT Capitole), Université de Toulouse (UT), Pacte, Laboratoire de sciences sociales (PACTE), Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Sciences Po Grenoble - Institut d'études politiques de Grenoble (IEPG), Université Grenoble Alpes (UGA), University of Oslo (UiO), Laboratoire des sciences de l'ingénieur, de l'informatique et de l'imagerie (ICube), École Nationale du Génie de l'Eau et de l'Environnement de Strasbourg (ENGEES)-Université de Strasbourg (UNISTRA)-Institut National des Sciences Appliquées - Strasbourg (INSA Strasbourg), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Les Hôpitaux Universitaires de Strasbourg (HUS)-Centre National de la Recherche Scientifique (CNRS)-Matériaux et Nanosciences Grand-Est (MNGE), Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut de Chimie du CNRS (INC)-Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut de Chimie du CNRS (INC)-Centre National de la Recherche Scientifique (CNRS)-Réseau nanophotonique et optique, Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA)-Centre National de la Recherche Scientifique (CNRS), Institut des Géosciences de l’Environnement (IGE), Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Laboratoire d'études en Géophysique et océanographie spatiales (LEGOS), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Centre National de la Recherche Scientifique (CNRS), Institut de Physique du Globe de Paris (IPGP (UMR_7154)), Institut national des sciences de l'Univers (INSU - CNRS)-Université de La Réunion (UR)-Institut de Physique du Globe de Paris (IPG Paris)-Centre National de la Recherche Scientifique (CNRS)-Université Paris Cité (UPCité), Laboratoire Image, Ville, Environnement (LIVE), Université de Strasbourg (UNISTRA)-Centre National de la Recherche Scientifique (CNRS), Dynamiques et écologie des paysages agriforestiers (DYNAFOR), École nationale supérieure agronomique de Toulouse (ENSAT), Institut National Polytechnique (Toulouse) (Toulouse INP), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Kermap [Rennes], Institut National de l'Information Géographique et Forestière [IGN] (IGN), Institut de Recherche pour le Développement (IRD), Territoires, Environnement, Télédétection et Information Spatiale (UMR TETIS), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-AgroParisTech-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Département Environnements et Sociétés (Cirad-ES), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), UMR 228 Espace-Dev, Espace pour le développement, Institut de Recherche pour le Développement (IRD)-Université de Perpignan Via Domitia (UPVD)-Avignon Université (AU)-Université de La Réunion (UR)-Université de Guyane (UG)-Université des Antilles (UA)-Université de Montpellier (UM), Littoral, Environnement, Télédétection, Géomatique UMR 6554 (LETG), Université de Brest (UBO)-Université de Rennes 2 (UR2)-Centre National de la Recherche Scientifique (CNRS)-Institut de Géographie et d'Aménagement Régional de l'Université de Nantes (Nantes Univ - IGARUN), Nantes Université - pôle Humanités, Nantes Université (Nantes Univ)-Nantes Université (Nantes Univ)-Nantes Université - pôle Humanités, Nantes Université (Nantes Univ)-Nantes Université (Nantes Univ), Centre National de la Recherche Scientifique (CNRS), CNES - Centre national d'études spatiales, CESBIO, Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Université Fédérale Toulouse Midi-Pyrénées-Météo-France -Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Université Toulouse 1 Capitole (UT1), Université Fédérale Toulouse Midi-Pyrénées, Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Sciences Po Grenoble - Institut d'études politiques de Grenoble (IEPG ), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Matériaux et nanosciences d'Alsace (FMNGE), Institut de Physique du Globe de Paris (IPGP), Institut national des sciences de l'Univers (INSU - CNRS)-IPG PARIS-Université de La Réunion (UR)-Centre National de la Recherche Scientifique (CNRS)-Université Paris Cité (UPCité), Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Université Fédérale Toulouse Midi-Pyrénées-Météo-France -Institut de Recherche pour le Développement (IRD)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Centre National de la Recherche Scientifique (CNRS), Empenn, Institut National de la Santé et de la Recherche Médicale (INSERM)-Inria Rennes – Bretagne Atlantique, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-SIGNAL, IMAGE ET LANGAGE (IRISA-D6), Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Rennes 1 (UR1), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT), Université de Brest (UBO)-Université de Rennes 2 (UR2), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Centre National de la Recherche Scientifique (CNRS)-Institut de Géographie et d'Aménagement Régional de l'Université de Nantes (NantesUniv – IGARUN), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Centre National de la Recherche Scientifique (CNRS), École nationale supérieure agronomique de Toulouse [ENSAT]-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Université de Brest (UBO)-Université de Rennes 2 (UR2)-Centre National de la Recherche Scientifique (CNRS)-Institut de Géographie et d'Aménagement Régional de l'Université de Nantes (NantesUniv – IGARUN), and Michel, Julien

Subjects
Télédétection, Time archive, [SDU.STU] Sciences of the Universe [physics]/Earth Sciences, [SDU.STU]Sciences of the Universe [physics]/Earth Sciences, Radiometry
Published
2022

39. Deep Learning for Automatic Colorization of Legacy Grayscale Aerial Photographs

Author

Grzegorz Skupinski, Quentin Poterek, Pierre-Alexis Herrault, David Sheeren, Laboratoire Image, Ville, Environnement (LIVE), Université de Strasbourg (UNISTRA)-Centre National de la Recherche Scientifique (CNRS), Dynamiques et écologie des paysages agriforestiers (DYNAFOR), École nationale supérieure agronomique de Toulouse [ENSAT]-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), and Zone Atelier Environnementale Urbaine part of the French Long Term Ecosystem Research Network

Subjects
Atmospheric Science, Computer science, Geophysics. Cosmic physics, 02 engineering and technology, Land cover, 010501 environmental sciences, 01 natural sciences, Grayscale, remote sensing, [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, 0202 electrical engineering, electronic engineering, information engineering, Colorization, Computer vision, Computers in Earth Sciences, Spectral data, TC1501-1800, 0105 earth and related environmental sciences, grayscale imagery, QC801-809, business.industry, Deep learning, Photography, deep learning, historical aerial photograph, 15. Life on land, Random forest, Ocean engineering, 020201 artificial intelligence & image processing, Artificial intelligence, business, generative adversarial network (GAN), Generative adversarial network
Abstract

Legacy grayscale aerial photographs represent one of the main available sources for studying the past state of the environment and its relationship to the present. However, these photographs lack spectral information thereby hindering their use in current remote sensing approaches that rely on spectral data for characterizing surfaces. This article proposes a conditional generative adversarial network, a deep learning model, to enrich legacy photographs by predicting color channels for an input grayscale image. The technique was used to colorize two orthophotographs (taken in 1956 and 1978) covering the entire Eurométropole de Strasbourg. To assess the model's performances, two strategies were proposed: first, colorized photographs were evaluated with metrics such as peak signal-to-noise ratio (PSNR), and structural similarity index (SSIM); second, random forest classifications were performed to extract land cover classes from grayscale and colorized photographs, respectively. The results revealed strong performances, with PSNR = 25.56 ± 2.20 and SSIM = 0.93 ± 0.06 indicating that the model successfully learned the mapping between grayscale and color photographs over a large territory. Moreover, land cover classifications performed on colorized data showed significant improvements over grayscale photographs, respectively, +6% and +17% for 1956 and 1978. Finally, the plausibility of outputs images was evaluated visually. We conclude that deep learning models are powerful tools for improving radiometric properties of old aerial grayscale photographs and land cover mapping. We also argue that the proposed approach could serve as a basis for further developments aiming to promote the use of aerial photographs archives for landscapes reconstruction.

Published
2020
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40. Individual Tree Crown Delineation Method Based on Multi-Criteria Graph Using Geometric and Spectral Information: Application to Several Temperate Forest Sites

Author

Fabre, Matthieu Deluzet, Thierry Erudel, Xavier Briottet, David Sheeren, and Sophie

Subjects
individual tree crown, delineation, LiDAR, passive optical image, temperate forest, coniferous, deciduous
Abstract

Individual tree crown (ITC) delineation in temperate forests is challenging owing to the presence of broadleaved species with overlapping crowns. Mixed coniferous/deciduous forests with characteristics that differ with the type of tree thus require a flexible method of delineation. The ITC delineation method based on the multi-criteria graph (MCG-Tree) addresses this problem in temperate monospecific or mixed forests by combining geometric and spectral information. The method was used to segment trees in three temperate forest sites with different characteristics (tree types, species distribution, planted or natural forest). Compared with a state-of-the-art watershed segmentation approach, our method increased delineation performance by up to 25%. Our results showed that the main geometric criterion to improve delineation quality is related to the crown radius (performance improvement around 8%). Coniferous/deciduous classification automatically adapts the MCG-Tree criteria to the type of tree. Promising results are then obtained to improve delineation performance for mixed forests.

Published
2022
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42. Éditorial.

Author

Michel Deshayes, Anne Puissant, and David Sheeren

Published
2012

44. Spatial and Temporal Domain Adaptation by Optimal Transport for Mapping Poplar Plantations Over Large Areas

Author

A. Yousra Hamrouni and B. David Sheeren

Subjects
Domain adaptation, Computer science, business.industry, Temporal resolution, Statistics, Distribution (economics), Rotation cycle, Scale (map), business, Data modeling
Abstract

Accurate estimates of poplar plantations area and their distribution are not available at the French national scale due to their short rotation cycle (15 years on average) and the low update rate of existing forest databases. The availability of high spatial, spectral and temporal resolution Sentinel-2 images has opened up new opportunities for classifying poplar plantations over large areas. When supervised classification models, trained on a specific area or over a given time period, are used to predict a different region or different periods of time, the predictive performance drops off due to data shift issues. Domain adaptation techniques and in particular optimal transport (OT) are proposed in this study to overcome the transfer problem aforementioned. OT was performed between two different Sentinel-2 tiles located in the north-east and southwest of France and according to two transfer modes: spatial (i.e. same year, different sites) and spatio-temporal (i.e. different years, different sites). The main objective is to assess the potential of OT to adapt the data distribution of a source tile to better identify poplar plantations on a target tile where little knowledge is available. The results show the ability of spatial OT to improve the producer's accuracy of the poplar class up to 27% as well as an increase in the classification's overall accuracy up to 35%. The improvement is particularly important when source and target labels are considered. OT proved to be of little relevance for classes that were initially very well identified including poplar plantations, but it showed all its potential for classes that were harder to classify, which consequently improved the global classification performance.

Published
2021
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45. Graph-Based Approach to Improve Individual Tree Crown Delineation in Temperate Forest using Structural And Spectral Information

Author

Thomas Houet, Thierry Erudel, David Sheeren, Xavier Briottet, Sophie Fabre, M. Deluzet, ONERA / DOTA, Université de Toulouse [Toulouse], ONERA-PRES Université de Toulouse, Dynamiques et écologie des paysages agriforestiers (DYNAFOR), École nationale supérieure agronomique de Toulouse [ENSAT]-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Littoral, Environnement, Télédétection, Géomatique UMR 6554 (LETG), Université de Nantes (UN)-Université de Brest (UBO)-Université de Rennes 2 (UR2), and Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Centre National de la Recherche Scientifique (CNRS)

Subjects
Watershed, LiDAR, 010504 meteorology & atmospheric sciences, 0211 other engineering and technologies, 02 engineering and technology, 01 natural sciences, Individual tree crown, [SPI]Engineering Sciences [physics], Délinéation, Segmentation, Forest, 021101 geological & geomatics engineering, 0105 earth and related environmental sciences, Mathematics, [PHYS]Physics [physics], Graph rewriting, business.industry, Hyperspectral imaging, Temperate forest, Pattern recognition, Image segmentation, Delineation, 15. Life on land, Tree (graph theory), Hyperspectral, Forêt, Artificial intelligence, Scale (map), business
Abstract

International audience; Forest characterization at tree scale is possible with remotely sensed images of high spatial resolution. A first step is the Individual Tree Crown (ITC) delineation which corresponds to the segmentation of canopy cover into different tree crowns. In this study, a new method based on an initial watershed segmentation of Canopy Height Model (CHM) is proposed. This method is based on a graph transformation of the initial segmentation map in order to apply structural criteria (calculated owning to CHM) and spectral criterion (computed on red, green and blue bands). Adaptive thresholds are defined according to the studied forest characteristics in order to improve low quality segmentation cases. This method has been applied on a complex forest site (Broadleaf dominant, steep relief…). The results show a 17% increase of global performance in comparison to the reference watershed segmentation.; La caractérisation de la forêt à l'échelle de l'arbre est possible avec des images de télédétection de haute résolution spatiale. Une première étape est la délimitation de la couronne individuelle des arbres (ITC) qui correspond à la segmentation du couvert forestier en différentes couronnes d'arbres. Dans cette étude, une nouvelle méthode basée sur une segmentation initiale des bassins versants du modèle de hauteur de la canopée (CHM) est proposée. Cette méthode est basée sur une transformation graphique de la carte de segmentation initiale afin d'appliquer des critères structurels (calculés en propriété au CHM) et spectraux (calculés sur des bandes rouges, vertes et bleues). Des seuils adaptatifs sont définis en fonction des caractéristiques forestières étudiées afin d'améliorer les cas de segmentation de faible qualité. Cette méthode a été appliquée sur un site forestier complexe (dominante feuillue, relief escarpé…). Les résultats montrent une augmentation de 17% de la performance globale par rapport à la segmentation des bassins versants de référence.

Published
2021
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48. Optimising spatial distribution of mass‐flowering patches at the landscape scale to increase crop pollination

Author

James Desaegher, David Sheeren, Annie Ouin, Ecologie Systématique et Evolution (ESE), AgroParisTech-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Dynamiques et écologie des paysages agriforestiers (DYNAFOR), École nationale supérieure agronomique de Toulouse [ENSAT]-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), and Occitanie Région

Subjects
0106 biological sciences, InVEST, pollination, Pollination, Scale (ratio), media_common.quotation_subject, [SDV]Life Sciences [q-bio], Spatial distribution, mass-flowering crops, 010603 evolutionary biology, 01 natural sciences, Competition (biology), Ecological intensification, wildflower strips, facilitation, ecological intensification, media_common, agriculture, 2. Zero hunger, Ecology, Agroforestry, business.industry, 010604 marine biology & hydrobiology, 15. Life on land, Geography, Agriculture, Crop pollination, business, competition
Abstract

International audience; The addition of floral resources is often recommended to improve entomophilous crop pollination. However, implanting new plots of mass-flowering resources in landscapes can have both positive and negative effects on pollinator visitation rates to crops.We investigated the effects of plot characteristics (size and nesting quantities) on the best places to locate new co-flowering plots to optimise crop pollination. First, we parameterised and validated an existing pollination model with field data. We set up experiments to simulate the conversion of random plots into new sunflower fields vs wildflower plots (intended to provide more nesting sites than sunflower fields and co-flowering with sunflower) and quantified the change in pollinator visitation rates to existing target sunflower fields.We predicted a decrease in visitation rates (competition) when floral plots were established next to target fields and an increase in visitation rates (facilitation) when floral plots were established farther away. We identified the competition-facilitation transition distance (CF distance), and the distance at which facilitation was maximal (Fmax distance). We interpreted competition effects as a dilution of pollinators among the floral resources and facilitation effects as an increase in habitat quality, thus supporting bigger pollinator populations. Implanting floral resources that provide more nesting sites, such as wildflower plots vs new sunflower fields, significantly increased facilitation and reduced CF distance (1,241 m vs 713 m) and Fmax distance (1,788 m vs 1,138 m). The size of the converted plot did not affect CF or Fmax distance.We predicted a benefit in isolating rather than aggregating multiple sunflower fields in terms of global crop pollination.Synthesis and applications. Our findings highlight the opportunity to optimise the spatial distribution of mass-flowering resources at the landscape scale to enhance crop pollination. We also suggest preserving or introducing nesting sites as a priority in areas with high-density mass flowering resources to compensate for the negative consequences of pollinator dilution.

Published
2021
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49. Contribution of remote sensing to the calculation of agri-environmental indicators for the CAP, farmers and stakeholders

Author

Christian Bockstaller, Clélia Sirami, David Sheeren, Olivier Keichinger, Ludovic Arnaud, Favreau Arthur, Frédérique Angevin, Laurent Dominique, Marchand Guillaume, Emmanuel Delaroche, Eric Ceschia, Laboratoire Agronomie et Environnement - Antenne Colmar (LAE-Colmar ), Laboratoire Agronomie et Environnement (LAE), Université de Lorraine (UL)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université de Lorraine (UL)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Dynamiques et écologie des paysages agriforestiers (DYNAFOR), École nationale supérieure agronomique de Toulouse (ENSAT), Institut National Polytechnique (Toulouse) (Toulouse INP), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Chercheur indépendant, Centre d'études spatiales de la biosphère (CESBIO), Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Institut National de l'Information Géographique et Forestière [IGN] (IGN), Unité Impacts Ecologiques des Innovations en Production Végétale (ECO-INNOV), Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Agence de Services et de Paiement (ASP), European Project: 727482,DiverIMPACTS, Université de Lorraine (UL)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), École nationale supérieure agronomique de Toulouse [ENSAT]-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), and Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)

Subjects
Vegetation indices, [SDV.EE]Life Sciences [q-bio]/Ecology, environment, Carbon budget, Bilan carbone, NIVA project, NDVI, [SDV]Life Sciences [q-bio], Indices de végétation, Biodiversity, Biodiversité, Projet NIVA, Nitrate
Abstract

Ce numéro est constitué d’articles issus du colloque « Le spatial au service de la transition agro-écologique » organisé le 16 décembre 2020, en Webinaire et en partenariat avec le CNES.; International audience; The development of multicriteria evaluation methods is one of the steps to support the current agroecological transition. The implementation of agri-environmental indicators at regional scale faces a major obstacle which is the acquisition of data on management, soil description, vegetation, and even climate, at fine resolution (field, farm) and over large areas (region, country). The use of satellite data is a major solution to remove this obstacle, especially with the deployment since 2014 of a new generation of satellites making it possible to observe earth with high-resolution images; the Sentinels. After a short review of the possibilities provided by these new satellites, the article presents the progress on the development of agri-environmental indicators calculable with help of remote sensing. A focus is given on three indicators in the H2020 NIVA project: carbon budget, nitrate leaching and biodiversity.; Le développement de méthodes d’évaluation multicritère est une des étapes pour accompagner et soutenir la transition agroécologique en cours. La mise en œuvre d’indicateurs agri-environnementaux au niveau territorial se heurte à un obstacle opérationnel qui est la phase d’acquisition des données sur les pratiques, la description des sols, la végétation voire des données climatiques et ceci que ce soit à des résolutions fines (parcelles, exploitation) ou sur de grandes étendues (département, région ou pays). Le recours à des données satellitaires constitue une piste majeure pour lever ce verrou, notamment avec l’arrivée à partir de 2014 d’une nouvelle génération de satellites à haute résolution d’observation de la terre : les Sentinel. Après un bref état de l’art des possibilités offertes par ces nouveaux satellites, l’article présente les avancées sur le développement d’indicateurs agri-environnementaux calculables au moyen de la télédétection. Un focus est mis sur trois indicateurs dans le cadre du projet H2020 NIVA: bilan carbone, risque de lixiviation des nitrates et biodiversité.

Published
2021
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50. Submerged macrophyte assessment in rivers: An automatic mapping method using Pléiades imagery

Author

Arnaud Elger, Stephanie Courty, Yves Auda, David Sheeren, Diane Espel, Laboratoire Ecologie Fonctionnelle et Environnement (ECOLAB), Institut Ecologie et Environnement (INEE), Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Observatoire Midi-Pyrénées (OMP), Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées, Adict Solutions, Géosciences Environnement Toulouse (GET), Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD), Dynamiques et écologie des paysages agriforestiers (DYNAFOR), École nationale supérieure agronomique de Toulouse [ENSAT]-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), École nationale supérieure agronomique de Toulouse [ENSAT], Agence de l'Eau Adour-Garonne, Association Nationale de la Recherche et de la Technologie, French National Research Agency (ANR), Laboratoire Ecologie Fonctionnelle et Environnement (LEFE), Université de Toulouse (UT)-Université de Toulouse (UT)-Observatoire Midi-Pyrénées (OMP), Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université de Toulouse (UT), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Centre National de la Recherche Scientifique (CNRS), École nationale supérieure agronomique de Toulouse (ENSAT), Institut National Polytechnique (Toulouse) (Toulouse INP), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), and Université de Toulouse (UT)-Université de Toulouse (UT)

Subjects
Satellite Imagery, Environmental Engineering, [SDV]Life Sciences [q-bio], 0208 environmental biotechnology, Multispectral image, Support Vector Regression, 02 engineering and technology, 010501 environmental sciences, 01 natural sciences, Fluvial ecosystem, Normalized Difference Vegetation Index, Rivers, Machine learning, Satellite imagery, 14. Life underwater, Waste Management and Disposal, Ecosystem, Aquatic vegetation, 0105 earth and related environmental sciences, Water Science and Technology, Civil and Structural Engineering, Remote sensing, Random Forest, Ecological Modeling, Sampling (statistics), Spectral bands, Vegetation, 15. Life on land, Pollution, 6. Clean water, 020801 environmental engineering, Macrophyte, Random forest, 13. Climate action, Remote Sensing Technology, Environmental science, Environmental Monitoring
Abstract

International audience; Submerged macrophyte monitoring is a major concern for hydrosystem management, particularly for understanding and preventing the potential impacts of global change on ecological functions and services. Macrophyte distribution assessments in rivers are still primarily realized using field monitoring or manual photo-interpretation of aerial images. Considering the lack of applications in fluvial environments, developing operational, low-cost and less time-consuming tools able to automatically map and monitor submerged macrophyte distribution is therefore crucial to support effective management programs. In this study, the suitability of very fine-scale resolution (50 cm) multispectral Pléiades satellite imagery to estimate submerged macrophyte cover, at the scale of a 1 km river section, was investigated. The performance of nonparametric regression methods (based on two reliable and well-known machine learning algorithms for remote sensing applications, Random Forest and Support Vector Regression) were compared for several spectral datasets, testing the relevance of 4 spectral bands (red, green, blue and near-infrared) and two vegetation indices (the Normalized Difference Vegetation Index, NDVI, and the Green-Red Vegetation Index, GRVI), and for several field sampling configurations. Both machine learning algorithms applied to a Pléiades image were able to reasonably well predict macrophyte cover in river ecosystems with promising performance metrics (R² above 0.7 and RMSE around 20%). The Random Forest algorithm combined to the 4 spectral bands from Pléiades image was the most efficient, particularly for extreme cover values (0% and 100%). Our study also demonstrated that a larger number of fine-scale field sampling entities clearly involved better cover predictions than a smaller number of larger sampling entities.

Published
2020
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