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Mapping forest in the Swiss Alps treeline ecotone with explainable deep learning / Thiên-Anh Nguyen in Remote sensing of environment, vol 281 (November 2022)  

Public [article]  inRemote sensing of environment > vol 281 (November 2022) . - n° 113217 Titre : Mapping forest in the Swiss Alps treeline ecotone with explainable deep learning Type de document : Article/Communication Auteurs : Thiên-Anh Nguyen, Auteur ; Benjamin Kellenberger, Auteur ; Devis Tuia, Auteur Année de publication : 2022 Article en page(s) : n° 113217 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] Alpes [Termes IGN] apprentissage profond [Termes IGN] canopée [Termes IGN] carte forestière [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] écotone [Termes IGN] hauteur des arbres [Termes IGN] image à très haute résolution [Termes IGN] image aérienne [Termes IGN] image RVB [Termes IGN] inventaire forestier étranger (données) [Termes IGN] modèle numérique de surface de la canopée [Termes IGN] Suisse Résumé : (auteur) Forest maps are essential to understand forest dynamics. Due to the increasing availability of remote sensing data and machine learning models like convolutional neural networks, forest maps can these days be created on large scales with high accuracy. Common methods usually predict a map from remote sensing images without deliberately considering intermediate semantic concepts that are relevant to the final map. This makes the mapping process difficult to interpret, especially when using opaque deep learning models. Moreover, such procedure is entirely agnostic to the definitions of the mapping targets (e.g., forest types depending on variables such as tree height and tree density). Common models can at best learn these rules implicitly from data, which greatly hinders trust in the produced maps. In this work, we aim at building an explainable deep learning model for forest mapping that leverages prior knowledge about forest definitions to provide explanations to its decisions. We propose a model that explicitly quantifies intermediate variables like tree height and tree canopy density involved in the forest definitions, corresponding to those used to create the forest maps for training the model in the first place, and combines them accordingly. We apply our model to mapping forest types using very high resolution aerial imagery and lay particular focus on the treeline ecotone at high altitudes, where forest boundaries are complex and highly dependent on the chosen forest definition. Results show that our rule-informed model is able to quantify intermediate key variables and predict forest maps that reflect forest definitions. Through its interpretable design, it is further able to reveal implicit patterns in the manually-annotated forest labels, which facilitates the analysis of the produced maps and their comparison with other datasets. Numéro de notice : A2022-794 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.rse.2022.113217 Date de publication en ligne : 01/09/2022 En ligne : https://doi.org/10.1016/j.rse.2022.113217 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101928 [article]

Land use/land cover mapping from airborne hyperspectral images with machine learning algorithms and contextual information / Ozlem Akar in Geocarto international, vol 37 n° 22 ([10/10/2022])  

Public [article]  inGeocarto international > vol 37 n° 22 [10/10/2022] . - pp 6643 - 6670 Titre : Land use/land cover mapping from airborne hyperspectral images with machine learning algorithms and contextual information Type de document : Article/Communication Auteurs : Ozlem Akar, Auteur ; Esra Tunc Gormus, Auteur Année de publication : 2022 Article en page(s) : pp 6643 - 6670 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] carte d'occupation du sol [Termes IGN] carte de la végétation [Termes IGN] classification orientée objet [Termes IGN] classification par forêts d'arbres décisionnels [Termes IGN] classification par séparateurs à vaste marge [Termes IGN] filtre de Gabor [Termes IGN] image hyperspectrale [Termes IGN] matrice de co-occurrence [Termes IGN] niveau de gris (image) [Termes IGN] texture d'image [Termes IGN] transformation en ondelettes [Termes IGN] Turquie Résumé : (auteur) Land use and Land cover (LULC) mapping is one of the most important application areas of remote sensing which requires both spectral and spatial resolutions in order to decrease the spectral ambiguity of different land cover types. Airborne hyperspectral images are among those data which perfectly suits to that kind of applications because of their high number of spectral bands and the ability to see small details on the field. As this technology has newly developed, most of the image processing methods are for the medium resolution sensors and they are not capable of dealing with high resolution images. Therefore, in this study a new framework is proposed to improve the classification accuracy of land use/cover mapping applications and to achieve a greater reliability in the process of mapping land use map using high resolution hyperspectral image data. In order to achieve it, spatial information is incorporated together with spectral information by exploiting feature extraction methods like Grey Level Co-occurrence Matrix (GLCM), Gabor and Morphological Attribute Profile (MAP) on dimensionally reduced image with highest accuracy. Then, machine learning algorithms like Random Forest (RF) and Support Vector Machine (SVM) are used to investigate the contribution of texture information in the classification of high resolution hyperspectral images. In addition to that, further analysis is conducted with object based RF classification to investigate the contribution of contextual information. Finally, overall accuracy, producer’s/user’s accuracy, the quantity and allocation based disagreements and location and quantity based kappa agreements are calculated together with McNemar tests for the accuracy assessment. According to our results, proposed framework which incorporates Gabor texture information and exploits Discrete Wavelet Transform based dimensionality reduction method increase the overall classification accuracy up to 9%. Amongst individual classes, Gabor features boosted classification accuracies of all the classes (soil, road, vegetation, building and shadow) to 7%, 6%, 6%, 8%, 9%, and 24% respectively with producer’s accuracy. Besides, 17% and 10% increase obtained in user’s accuracy with MAP (area) feature in classifying road and shadow classes respectively. Moreover, when the object based classification is conducted, it is seen that the OA of pixel based classification is increased further by 1.07%. An increase between 2% and 4% is achieved with producer’s accuracy in soil, vegetation and building classes and an increase between 1% and 3% is achieved by user’s accuracy in soil, road, vegetation and shadow classes. In the end, accurate LULC map is produced with object based RF classification of gabor features added airborne hyperspectral image which is dimensionally reduced with DWT method. Numéro de notice : A2022-729 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2021.1944453 Date de publication en ligne : 09/11/2021 En ligne : https://doi.org/10.1080/10106049.2021.1944453 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101675 [article]

Using multi-temporal tree inventory data in eucalypt forestry to benchmark global high-resolution canopy height models. A showcase in Mato Grosso, Brazil / Adrián Pascual in Ecological Informatics, vol 70 (September 2022)  

Public [article]  inEcological Informatics > vol 70 (September 2022) Titre : Using multi-temporal tree inventory data in eucalypt forestry to benchmark global high-resolution canopy height models. A showcase in Mato Grosso, Brazil Type de document : Article/Communication Auteurs : Adrián Pascual, Auteur ; Frederico Tupinambá-Simões, Auteur ; Tiago de Conto, Auteur Année de publication : 2022 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] carte forestière [Termes IGN] Eucalyptus (genre) [Termes IGN] forêt tropicale [Termes IGN] Global Ecosystem Dynamics Investigation lidar [Termes IGN] hauteur des arbres [Termes IGN] incertitude des données [Termes IGN] inventaire forestier étranger (données) [Termes IGN] Mato Grosso [Termes IGN] modèle numérique de surface de la canopée [Vedettes matières IGN] Inventaire forestier Mots-clés libres : E. urograndis  E. urophylla x E. grandis, E. urophylla and E. camaldulensis x E. grandis Résumé : (auteur) The global monitoring of forest structure worldwide is increasingly being supported by refined and enhanced satellite mission datasets. Forest canopy height is a global metric to characterise and monitor dynamics in forest ecosystems worldwide. Satellite mapping missions as NASA's Global Ecosystem Dynamics Investigation (GEDI) are creating opportunities to refine global forest canopy height models adding forest structural information to time-series satellite imagery. A recent global canopy height model presented by Lang et al., (2022) using GEDI and 10-m Sentinel-2 and the map from Potapov et al., (2020) using GEDI and Landsat are both tested in this study using multi-temporal tree-level data collected over eucalypt plantations in Brazil. Our results at plot-level showed Lang et al., (2022)’s estimates of canopy height came short compared to 2020 maximum and mean tree height records in the plots, 7.6 and 3.6 m, respectively, but adding CHM standard deviation improves the agreement of ground records for maximum tree height. Higher errors were computed for the plots in 2019 using the Potapov's 30-m CHM: 14.2 and 9.5 m, respectively. Averaged stand values were more similar between the three sources tested. We report improvement from the 30-m CHM to the 10-m, but still height saturation problems were observed when accounting for height differences in tall eucalypt trees. As more global products for forest height and biomass are becoming available to users, more validation exercises as presented in this study are needed to assess the suitability of CHM products to forestry needs, and facilitate the uptake and actionability of the next generation of global height and biomass products. We provide recommendations and insights on the use of GEDI laser data for global mapping and on the potential of commercial forestry areas to benchmark the accuracy of satellite mapping missions focusing on tree height estimation in the tropics. Numéro de notice : A2022-615 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.ecoinf.2022.101748 En ligne : https://doi.org/10.1016/j.ecoinf.2022.101748 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101370 [article]

About tree height measurement: Theoretical and practical issues for uncertainty quantification and mapping / Samuele De petris in Forests, vol 13 n° 7 (July 2022)  

Public [article]  inForests > vol 13 n° 7 (July 2022) . - n°969 Titre : About tree height measurement: Theoretical and practical issues for uncertainty quantification and mapping Type de document : Article/Communication Auteurs : Samuele De petris, Auteur ; Philippo Sarvia, Auteur ; Enrico Borgogno Mondino, Auteur Année de publication : 2022 Article en page(s) : n°969 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] analyse de sensibilité [Termes IGN] biome [Termes IGN] carte forestière [Termes IGN] Google Earth Engine [Termes IGN] hauteur des arbres [Termes IGN] incertitude de mesurage [Termes IGN] modèle de simulation [Termes IGN] pente [Termes IGN] statistiques [Termes IGN] variance [Vedettes matières IGN] Foresterie Résumé : (auteur) Forest height is a fundamental parameter in forestry. Tree height is widely used to assess a site’s productivity both in forest ecology research and forest management. Thus, a precise height measure represents a necessary step for the estimation of carbon storage at the local, national, and global scales. In this context, error in height measurement necessarily affects the accuracy of related estimates. Ordinarily, forest height is surveyed by ground sampling adopting hypsometers. The latter suffers from many errors mainly related to the correct tree apex identification (not always well visible in dense stands) and to the measurement process itself. In this work, a statistically based operative method for estimating height measurement uncertainty (σH) was proposed using the variance propagation law. Some simulations were performed involving several combinations of terrain slope, tree height, and survey distances by modelling the σH behaviour and its sensitivity to such parameters. Results proved that σH could vary between 0.5 m and up to 20 m (worst case). Sensitivity analysis shows that terrain slopes and distance poorly affect σH, while angles are the main drivers of height uncertainty. Finally, to give a practical example of such deductions, tree height uncertainty was mapped at the global scale using Google Earth Engine and summarized per forest biomes. Results proved that tropical biomes have higher uncertainty (from 1 m to 4 m) while shrublands and tundra have the lowest (under 1 m). Numéro de notice : A2022-546 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article DOI : 10.3390/f13070969 Date de publication en ligne : 22/06/2022 En ligne : https://doi.org/10.3390/f13070969 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101131 [article]

The promising combination of a remote sensing approach and landscape connectivity modelling at a fine scale in urban planning / Elie Morin in Ecological indicators, vol 139 (June 2022)  

Public [article]  inEcological indicators > vol 139 (June 2022) . - n° 108930 Titre : The promising combination of a remote sensing approach and landscape connectivity modelling at a fine scale in urban planning Type de document : Article/Communication Auteurs : Elie Morin, Auteur ; Pierre-Alexis Herrault, Auteur ; Yvonnick Guinard, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 108930 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] analyse d'image orientée objet [Termes IGN] analyse du paysage [Termes IGN] BD Topo [Termes IGN] carte d'occupation du sol [Termes IGN] carte de la végétation [Termes IGN] classification orientée objet [Termes IGN] classification par forêts d'arbres décisionnels [Termes IGN] connexité (topologie) [Termes IGN] corridor biologique [Termes IGN] extraction de la végétation [Termes IGN] extraction de traits caractéristiques [Termes IGN] indicateur environnemental [Termes IGN] milieu urbain [Termes IGN] Niort [Termes IGN] planification urbaine [Termes IGN] Poitiers [Termes IGN] segmentation d'image [Termes IGN] Vienne (86) Résumé : (auteur) Urban landscapes are rapid changing ecosystems with diverse urban forms that impede the movement of organisms. Therefore, designing and modelling ecological networks to identify biodiversity reservoirs and their corridors are crucial aspects of land management in terms of population persistence and survival. However, the land cover/use maps used for landscape connectivity modelling can lack information in such a highly complex environment. In this context, remote sensing approaches are gaining interest for the development of accurate land cover/use maps. We tested the efficiency of an object-based classification using open-source projects and free images to identify vegetation strata at a very fine scale and evaluated its contribution to landscape connectivity modelling. We compared different spatial and thematic resolutions from existing databases and object-based image analyses in three French cities. Our results suggested that this remote sensing approach produced reliable land cover maps to differentiate artificial areas, tree vegetation and herbaceous vegetation. Land cover maps enhanced with the remote sensing products substantially changed the structural connectivity indices, showing an improvement up to four times the proportion of herbaceous and tree vegetation. In addition, functional connectivity indices evaluated for several forest species were mainly impacted for medium dispersers in quantitative (metrics) and qualitative (corridors) estimations. Thus, the combination of this reproductible remote sensing approach and landscape connectivity modelling at a very fine scale provides new insights into the characterisation of ecological networks for conservation planning. Numéro de notice : A2022-368 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE/URBANISME Nature : Article DOI : 10.1016/j.ecolind.2022.108930 Date de publication en ligne : 04/05/2022 En ligne : https://doi.org/10.1016/j.ecolind.2022.108930 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100592 [article]

Classification of vegetation classes by using time series of Sentinel-2 images for large scale mapping in Cameroon / Hermann Tagne in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol V-3-2022 (2022 edition)  

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Vegetation cover mapping from RGB webcam time series for land surface emissivity retrieval in high mountain areas / Benedikt Hiebl in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol V-2-2022 (2022 edition)  

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Fusion of optical, radar and waveform LiDAR observations for land cover classification / Huiran Jin in ISPRS Journal of photogrammetry and remote sensing, vol 187 (May 2022)  

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Production of optimum forest roads and comparison of these routes with current forest roads: a case study in Maçka, Turkey / Faruk Yildirim in Geocarto international, vol 37 n° 8 ([01/05/2022])  

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Crop type identification and spatial mapping using Sentinel-2 satellite data with focus on field-level information / Murali Krishna Gumma in Geocarto international, vol 37 n° 7 ([15/04/2022])  

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Parcel-based summer maize mapping and phenology estimation combined using Sentinel-2 and time series Sentinel-1 data / Yanyan Wang in International journal of applied Earth observation and geoinformation, vol 108 (April 2022)  

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Species level classification of Mediterranean sparse forests-maquis formations using Sentinel-2 imagery / Semiha Demirbaş Çağlayana in Geocarto international, vol 37 n° 6 ([01/04/2022])  

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Travaux actuels d'inventaire des forêts à forte naturalité à l'échelle nationale et européenne / Fabienne Benest in Revue forestière française, vol 73 n° 2 - 3 ([30/03/2022])  

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Mapping forest site quality at national level / Ana Aguirre in Forest ecology and management, vol 508 (March-15 2022)  

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Ultrahigh-resolution boreal forest canopy mapping: Combining UAV imagery and photogrammetric point clouds in a deep-learning-based approach / Linyuan Li in International journal of applied Earth observation and geoinformation, vol 107 (March 2022)  

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