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Applications of remote sensing data in mapping of forest growing stock and biomass / Jose Aranha (2021)
Titre : Applications of remote sensing data in mapping of forest growing stock and biomass Type de document : Monographie Auteurs : Jose Aranha, Éditeur scientifique Editeur : Bâle [Suisse] : Multidisciplinary Digital Publishing Institute MDPI Année de publication : 2021 Importance : 276 p. Format : 16 x 24 cm ISBN/ISSN/EAN : 978-3-0365-0569-5 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] apprentissage automatique
[Termes IGN] biomasse aérienne
[Termes IGN] capital sur pied
[Termes IGN] carte forestière
[Termes IGN] Chine
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] données lidar
[Termes IGN] foresterie
[Termes IGN] forêt boréale
[Termes IGN] image captée par drone
[Termes IGN] image Landsat-OLI
[Termes IGN] image radar moirée
[Termes IGN] image Sentinel-MSI
[Termes IGN] image SPOT 6
[Termes IGN] inventaire forestier étranger (données)
[Termes IGN] Pinus massoniana
[Termes IGN] puits de carbone
[Termes IGN] service écosystémique
[Termes IGN] système d'information géographique
[Termes IGN] ThaïlandeRésumé : (éditeur) This Special Issue (SI), entitled "Applications of Remote Sensing Data in Mapping of Forest Growing Stock and Biomass”, resulted from 13 peer-reviewed papers dedicated to Forestry and Biomass mapping, characterization and accounting. The papers' authors presented improvements in Remote Sensing processing techniques on satellite images, drone-acquired images and LiDAR images, both aerial and terrestrial. Regarding the images’ classification models, all authors presented supervised methods, such as Random Forest, complemented by GIS routines and biophysical variables measured on the field, which were properly georeferenced. The achieved results enable the statement that remote imagery could be successfully used as a data source for regression analysis and formulation and, in this way, used in forestry actions such as canopy structure analysis and mapping, or to estimate biomass. This collection of papers, presented in the form of a book, brings together 13 articles covering various forest issues and issues in forest biomass calculation, constituting an important work manual for those who use mixed GIS and RS techniques. Note de contenu : 1- Finer resolution estimation and mapping of mangrove biomass using UAV LiDAR and WorldView-2 data
2- Nondestructive estimation of the above-ground biomass of multiple tree species in boreal forests of China using Terrestrial Laser Scanning
3- Estimating forest aboveground carbon storage in Hang-Jia-Hu using Landsat TM/OLI data and random morest Model
4- Influence of variable selection and forest type on forest aboveground biomass estimation using machine learning algorithms
5- Comparative analysis of seasonal Landsat 8 images for forest aboveground biomass estimation in a subtropical forest
6- Estimating urban vegetation biomass from Sentinel-2A image data
7- Estimation of forest biomass in Beijing (China) using multisource remote sensing and forest inventory data
8- Spatially explicit analysis of trade-offs and synergies among multiple ecosystem services in Shaanxi Valley basin
9- Influence of site-specific conditions on estimation of forest above ground biomass from airborne laser scanning
10- Multi-sensor prediction of stand volume by a hybrid model of support vector machine for regression kriging
11- Applying LiDAR to quantify the plant area index along a successional gradient in a tropical forest of Thailand
12- Shrub biomass estimates in former burnt areas using Sentinel 2 images processing and classification
13- Evaluation of different algorithms for estimating the growing stock volume of pinus massoniana plantations using spectral and spatial information from a SPOT6 imageNuméro de notice : 15305 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Recueil / ouvrage collectif DOI : 10.3390/books978-3-0365-0569-5 En ligne : https://doi.org/10.3390/books978-3-0365-0569-5 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99903
Titre : Artificial neural networks and evolutionary computation in remote sensing Type de document : Monographie Auteurs : Taskin Kavzoglu, Éditeur scientifique Editeur : Bâle [Suisse] : Multidisciplinary Digital Publishing Institute MDPI Année de publication : 2021 Importance : 256 p. Format : 16 x 24 cm ISBN/ISSN/EAN : 978-3-03943-828-0 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] apprentissage automatique
[Termes IGN] apprentissage profond
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] image captée par drone
[Termes IGN] image hyperspectrale
[Termes IGN] image satellite
[Termes IGN] réseau antagoniste génératif
[Termes IGN] réseau neuronal artificiel
[Termes IGN] segmentation sémantiqueRésumé : (éditeur) Artificial neural networks (ANNs) and evolutionary computation methods have been successfully applied in remote sensing applications since they offer unique advantages for the analysis of remotely-sensed images. ANNs are effective in finding underlying relationships and structures within multidimensional datasets. Thanks to new sensors, we have images with more spectral bands at higher spatial resolutions, which clearly recall big data problems. For this purpose, evolutionary algorithms become the best solution for analysis. This book includes eleven high-quality papers, selected after a careful reviewing process, addressing current remote sensing problems. In the chapters of the book, superstructural optimization was suggested for the optimal design of feedforward neural networks, CNN networks were deployed for a nanosatellite payload to select images eligible for transmission to ground, a new weight feature value convolutional neural network (WFCNN) was applied for fine remote sensing image segmentation and extracting improved land-use information, mask regional-convolutional neural networks (Mask R-CNN) was employed for extracting valley fill faces, state-of-the-art convolutional neural network (CNN)-based object detection models were applied to automatically detect airplanes and ships in VHR satellite images, a coarse-to-fine detection strategy was employed to detect ships at different sizes, and a deep quadruplet network (DQN) was proposed for hyperspectral image classification. Note de contenu : 1- CloudScout: A deep neural network for on-board cloud detection on hyperspectral images
2- Machine learning classification ensemble of multitemporal Sentinel-2 images: The case of a mixed Mediterranean ecosystem
3- Computer vision and deep learning techniques for the analysis of drone-acquired forest images, a transfer learning study
4- Improved SRGAN for remote sensing image super-resolution across locations and sensors
5- Design of feedforward neural networks in the classification of hyperspectral imagery using superstructural optimization
6- Deep quadruplet network for hyperspectral image classification with a small number of samples
7- Mapping the topographic features of mining-related Valley Fills using mask R-CNN deep learning and digital elevation data
8- Improved winter wheat spatial distribution extraction from high-resolution remote sensing imagery using semantic features and statistical analysis
9- Comparative research on deep learning approaches for airplane detection from very high-resolution satellite images
10- A coarse-to-fine network for ship detection in optical remote sensing images
11- Improved remote sensing image classification based on multi-scale feature fusionNuméro de notice : 28443 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Recueil / ouvrage collectif DOI : 10.3390/books978-3-03943-828-0 En ligne : https://doi.org/10.3390/books978-3-03943-828-0 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98893 Assessment of combining convolutional neural networks and object based image analysis to land cover classification using Sentinel 2 satellite imagery (Tenes region, Algeria) / N. Zaabar (2021)
Titre : Assessment of combining convolutional neural networks and object based image analysis to land cover classification using Sentinel 2 satellite imagery (Tenes region, Algeria) Type de document : Article/Communication Auteurs : N. Zaabar, Auteur ; Simona Niculescu, Auteur ; M.K. Mihoubi, Auteur Editeur : International Society for Photogrammetry and Remote Sensing ISPRS Année de publication : 2021 Collection : International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, ISSN 1682-1750 num. 43-B2-2021 Conférence : ISPRS 2021, Commission 2, XXIV ISPRS Congress, Imaging today foreseeing tomorrow 05/07/2021 09/07/2021 Nice Virtuel France OA Archives Commission 2 Importance : pp 383 - 389 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] Algérie
[Termes IGN] analyse d'image orientée objet
[Termes IGN] carte d'occupation du sol
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] image Sentinel-MSI
[Termes IGN] littoral méditerranéen
[Termes IGN] villeRésumé : (auteur) Land cover maps can provide valuable information for various applications, such as territorial monitoring, environmental protection, urban planning and climate change prevention. In this purpose, remote sensing based on image classification approaches undergoing a high revolution can be dedicated to land cover mapping tasks. Similarly, deep learning models are considerably applied in remote sensing applications; which can automatically learn features from large amounts of data. Prevalently, the Convolutional Neural Network (CNN), have been increasingly performed in image classification. The aim of this study is to apply a new approach to analyse land cover, and extract its features. Experiments carried out on a coastal town located in north-western Algeria (Ténès region). The study area is chosen because of its importance as a part of the national strategy to combat natural hazards, specifically floods. As well as, a simple CNN model with two hidden layers was constructed, combined with an Object-Based Image Analysis (OBIA). In this regard, a Sentinel-2 image was used, to perform the classification, using spectral index combinations. Furthermore, to compare the performance of the proposed approach, an OBIA based on machines learning algorithms mainly Random Forest (RF) and Support Vector Machine (SVM), was provided. Results of accuracy assessment of classification showed good values in terms of Overall accuracy and Kappa Index, which reach to 93.1% and 0.91, respectively. As a comparison, CNN-OBIA approach outperformed OBIA based on RF algorithm. Therefore, Final land cover maps can be used as a support tool in regional and national decisions. Numéro de notice : C2021-020 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Communication DOI : 10.5194/isprs-archives-XLIII-B3-2021-383-2021 Date de publication en ligne : 28/06/2021 En ligne : https://doi.org/10.5194/isprs-archives-XLIII-B3-2021-383-2021 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98072 Automated detection of individual Juniper tree location and forest cover changes using Google Earth Engine / Sudeera Wickramarathna in Annals of forest research, vol 64 n° 1 (2021)
[article]
Titre : Automated detection of individual Juniper tree location and forest cover changes using Google Earth Engine Type de document : Article/Communication Auteurs : Sudeera Wickramarathna, Auteur ; Jamon Van Den Hoek, Auteur ; Bogdan Mihai Strimbu, Auteur Année de publication : 2021 Article en page(s) : pp 61 - 72 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] canopée
[Termes IGN] classification dirigée
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] couvert forestier
[Termes IGN] croissance des arbres
[Termes IGN] détection d'arbres
[Termes IGN] détection de changement
[Termes IGN] extraction de la végétation
[Termes IGN] Google Earth Engine
[Termes IGN] image à très haute résolution
[Termes IGN] image multibande
[Termes IGN] juniperus (genre)
[Termes IGN] Normalized Difference Vegetation Index
[Termes IGN] Normalized Difference Water Index
[Termes IGN] Oregon (Etats-Unis)
[Termes IGN] réflectanceRésumé : (auteur) Tree detection is the first step in the appraisal of a forest, especially when the focus is monitoring the growth of tree canopy. The acquisition of annual very high-resolution aerial images by the National Agriculture Imagery Program (NAIP) and their accessibility through Google Earth Engine (GEE) supports the delineation of tree canopies and change over time in a cost and time-effective manner. The objectives of this study are to develop an automated method to detect the crowns of individual western Juniper (Juniperus occidentalis) trees and to assess the change of forest cover from multispectral 1-meter resolution NAIP images collected from 2009 to 2016, in Oregon, USA. The Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), and Ratio Vegetation Index (RVI), were calculated from the NAIP images, in addition to the red-green-blue-near infrared bands. To identify the most suitable approach for individual tree crown identification, we created two training datasets: one considering yearly images separately and one merging all images, irrespective of the year. We segmented individual tree crowns using a random forest algorithm implemented in GEE and seven rasters, namely the reflectance of four spectral bands as recorded by the NAIP images (i.e., the red-green-blue-near infrared) and three calculated indices (i.e., NDVI, NDWI, and RVI). We compared the estimated location of the trees, computed as the centroid of the crown, with the visually identified treetops, which were considered as validation locations. We found that tree location errors were smaller when years were analyzed individually than by merging the years. Measurements of completeness (74%), correctness (94%), and mean accuracy detection (82 %) show promising performance of the random forest algorithm in crown delineation, considering that only four original input bands were used for crown segmentation. The change in the calculated crown area for western juniper follows a sinusoidal curve, with a decrease from 2011 to 2012 and an increase from 2012 to 2014. The proposed approach has the potential to estimate individual tree locations and forest cover area dynamics at broad spatial scales using regularly collected airborne imagery with easy-to-implement methods. Numéro de notice : A2021-779 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.15287/afr.2020.2145 Date de publication en ligne : 28/06/2021 En ligne : https://doi.org/10.15287/afr.2020.2145 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98846
in Annals of forest research > vol 64 n° 1 (2021) . - pp 61 - 72[article]Development and analysis of land-use/land-cover spatio-temporal metrics in urban environments: Exploring urban growth patterns and linkages to socio-economic factors / Marta Sapena Moll (2021)
Titre : Development and analysis of land-use/land-cover spatio-temporal metrics in urban environments: Exploring urban growth patterns and linkages to socio-economic factors Type de document : Thèse/HDR Auteurs : Marta Sapena Moll, Auteur ; Luis Angel Ruiz Fernandez, Directeur de thèse Editeur : Valencia : Universitat politécnica de Valencia Année de publication : 2021 Importance : 268 p. Format : 21 x 30 cm Note générale : bibliographie
PhD in Geomatics Engineering, Universidad politécnica de ValenciaLangues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] analyse discriminante
[Termes IGN] analyse spatio-temporelle
[Termes IGN] carte d'occupation du sol
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] croissance urbaine
[Termes IGN] données socio-économiques
[Termes IGN] implémentation (informatique)
[Termes IGN] milieu urbain
[Termes IGN] modélisation spatiale
[Termes IGN] occupation du sol
[Termes IGN] population urbaine
[Termes IGN] régression linéaire
[Termes IGN] Rhénanie du Nord-Wesphalie (Allemagne)
[Termes IGN] utilisation du sol
[Termes IGN] ville durableRésumé : (auteur) This thesis addresses the development and analysis of new tools and methods for monitoring and characterizing urban growth using geo-data and land-use/land-cover (LULC) databases, as well as exploring their relationships with socio-economic factors, providing new evidences regarding the use of LULC data for urban characterization at different levels by means of spatial and statistical methods. First, the most common spatio-temporal metrics were compiled and implemented within a software tool, IndiFrag. Then, we present a methodology based on spatio-temporal metrics and propose a new index that quantifies the inequality of growth between population and built-up areas to analyze and compare urban growth patterns at different levels. This allowed for a differentiation of growing patterns, besides, the analysis at various levels contributed to a better understanding of such patterns. Second, we quantified the two-way relationship between the urban structure in cities and their socio-economic status by means of spatial metrics issued from a local climate zone map for 31 cities in North Rhine-Westphalia, Germany. Based on these data, we quantified their relationship with socio-economic indicators by means of multiple linear regression models, explaining a significant part of their variability. The proposed method is transferable to other datasets, levels, and regions. Third, we assessed the use of spatio-temporal metrics derived from LULC maps to identify urban growth spatial patterns. We applied LULC change models to simulate different long-term scenarios of urban growth following various spatial patterns on diverse baseline urban forms. Then, we computed spatio-temporal metrics for the simulated scenarios, selected the most explanatory by applying a discriminant analysis and classified the growth patterns using clustering methods. Finally, we identified empirical relationships between socio-economic indicators and their change over time with the spatial structure of the built and natural elements in up to 600 urban areas from 32 countries. We employed random forest regression models and the spatio-temporal metrics were able to explain substantially the variability of socio-economic variables. This confirms that spatial patterns and their change are linked to socio-economic indicators. This work contributes to a better understanding of urban growth patterns and improves knowledge about the relationships between urban spatial structure and socio-economic factors, providing new methods for monitoring and assessing urban sustainability by means of LULC databases, which could be used by researchers, urban planners and decision-makers to ensure the sustainable future of urban environments. Note de contenu : 1- Introduction
2- Hypotheses and objectives
3- Spatio-temporal analysis of LULC and population in urban areas
4- Relationships between spatial patterns of urban structure and quality of life
5- Spatio-temporal metrics for urban growth spatial pattern categorization
6- Linking spatio-temporal metrics of built-up areas to socio-economic indicators on a semi-global scale
7- ConclusionsNuméro de notice : 28308 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/URBANISME Nature : Thèse étrangère Note de thèse : PhD Thesis : Geomatics Engineering : Valencia, Spain : 2021 Organisme de stage : German Aerospace Center DOI : 10.4995/Thesis/10251/158626 En ligne : https://doi.org/10.4995/Thesis/10251/158626 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98112 From local to global: A transfer learning-based approach for mapping poplar plantations at national scale using Sentinel-2 / Yousra Hamrouni in ISPRS Journal of photogrammetry and remote sensing, vol 171 (January 2021)PermalinkTélédétection et intégration de connaissances via la modélisation spatiale pour une cartographie plus cohérente des systèmes agricoles complexes / Arthur Crespin-Boucaud (2021)PermalinkUsing geometric and semantic attributes for semi-automated tag identification in OpenStreetMap data / Müslüm Hacar (2021)PermalinkApplication of various strategies and methodologies for landslide susceptibility maps on a basin scale: the case study of Val Tartano, Italy / Vasil Yordanov in Applied geomatics, vol 12 n° 4 (December 2020)PermalinkBioclimatic modeling of potential vegetation types as an alternative to species distribution models for projecting plant species shifts under changing climates / Robert E. Keane in Forest ecology and management, vol 477 ([01/12/2020])PermalinkExploring the inclusion of Sentinel-2 MSI texture metrics in above-ground biomass estimation in the community forest of Nepal / Santa Pandit in Geocarto international, vol 35 n° 16 ([01/12/2020])PermalinkA framework for unsupervised wildfire damage assessment using VHR satellite images with PlanetScope data / Minkyung Chung in Remote sensing, vol 12 n° 22 (December-1 2020)PermalinkMapping of land cover with open-source software and ultra-high-resolution imagery acquired with unmanned aerial vehicles / Ned Horning in Remote sensing in ecology and conservation, vol 6 n° 4 (December 2020)PermalinkMultistrategy ensemble regression for mapping of built-up density and height with Sentinel-2 data / Christian Geiss in ISPRS Journal of photogrammetry and remote sensing, vol 170 (December 2020)PermalinkCartographie des cultures dans le périmètre du Loukkos (Maroc) : apport de la télédétection radar et optique / Siham Acharki in Revue Française de Photogrammétrie et de Télédétection, n° 222 (novembre 2020)Permalink