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Wood decay detection in Norway spruce forests based on airborne hyperspectral and ALS data / Michele Dalponte in Remote sensing, vol 14 n° 8 (April-2 2022)  

Public [article]  inRemote sensing > vol 14 n° 8 (April-2 2022) . - n° 1892 Titre : Wood decay detection in Norway spruce forests based on airborne hyperspectral and ALS data Type de document : Article/Communication Auteurs : Michele Dalponte, Auteur ; Alvar J. I. Kallio, Auteur ; Hans Ole Ørka, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 1892 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques [Termes IGN] bois sur pied [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] dépérissement [Termes IGN] données d'entrainement (apprentissage automatique) [Termes IGN] données lidar [Termes IGN] image hyperspectrale [Termes IGN] image infrarouge [Termes IGN] Norvège [Termes IGN] Perceptron multicouche [Termes IGN] Picea abies [Termes IGN] régression linéaire [Termes IGN] régression logistique [Termes IGN] santé des forêts [Termes IGN] semis de points Résumé : (auteur) Wood decay caused by pathogenic fungi in Norway spruce forests causes severe economic losses in the forestry sector, and currently no efficient methods exist to detect infected trees. The detection of wood decay could potentially lead to improvements in forest management and could help in reducing economic losses. In this study, airborne hyperspectral data were used to detect the presence of wood decay in the trees in two forest areas located in Etnedal (dataset I) and Gran (dataset II) municipalities, in southern Norway. The hyperspectral data used consisted of images acquired by two sensors operating in the VNIR and SWIR parts of the spectrum. Corresponding ground reference data were collected in Etnedal using a cut-to-length harvester while in Gran, field measurements were collected manually. Airborne laser scanning (ALS) data were used to detect the individual tree crowns (ITCs) in both sites. Different approaches to deal with pixels inside each ITC were considered: in particular, pixels were either aggregated to a unique value per ITC (i.e., mean, weighted mean, median, centermost pixel) or analyzed in an unaggregated way. Multiple classification methods were explored to predict rot presence: logistic regression, feed forward neural networks, and convolutional neural networks. The results showed that wood decay could be detected, even if with accuracy varying among the two datasets. The best results on the Etnedal dataset were obtained using a convolution neural network with the first five components of a principal component analysis as input (OA = 65.5%), while on the Gran dataset, the best result was obtained using LASSO with logistic regression and data aggregated using the weighted mean (OA = 61.4%). In general, the differences among aggregated and unaggregated data were small. Numéro de notice : A2022-352 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE/INFORMATIQUE Nature : Article DOI : 10.3390/rs14081892 Date de publication en ligne : 14/04/2022 En ligne : https://doi.org/10.3390/rs14081892 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100541 [article]

Large-area inventory of species composition using airborne laser scanning and hyperspectral data / Hans Ole Ørka in Silva fennica, vol 55 n° 4 (September 2021)  

Public [article]  inSilva fennica > vol 55 n° 4 (September 2021) . - n° 10244 Titre : Large-area inventory of species composition using airborne laser scanning and hyperspectral data Type de document : Article/Communication Auteurs : Hans Ole Ørka, Auteur ; Endre H. Hansen, Auteur ; Michele Dalponte, Auteur ; Terje Gobakken, Auteur ; Erik Naesset, Auteur Année de publication : 2021 Article en page(s) : n° 10244 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] composition d'un peuplement forestier [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] image hyperspectrale [Termes IGN] inventaire forestier (techniques et méthodes) [Termes IGN] Norvège [Termes IGN] Picea abies [Termes IGN] Pinus sylvestris [Termes IGN] régression [Vedettes matières IGN] Inventaire forestier Résumé : (auteur) Tree species composition is an essential attribute in stand-level forest management inventories and remotely sensed data might be useful for its estimation. Previous studies on this topic have had several operational drawbacks, e.g., performance studied at a small scale and at a single tree-level with large fieldwork costs. The current study presents the results from a large-area inventory providing species composition following an operational area-based approach. The study utilizes a combination of airborne laser scanning and hyperspectral data and 97 field sample plots of 250 m2 collected over 350 km2 of productive forest in Norway. The results show that, with the availability of hyperspectral data, species-specific volume proportions can be provided in operational forest management inventories with acceptable results in 90% of the cases at the plot level. Dominant species were classified with an overall accuracy of 91% and a kappa-value of 0.73. Species-specific volumes were estimated with relative root mean square differences of 34%, 87%, and 102% for Norway spruce (Picea abies (L.) Karst.), Scots pine (Pinus sylvestris L.), and deciduous species, respectively. A novel tree-based approach for selecting pixels improved the results compared to a traditional approach based on the normalized difference vegetation index. Numéro de notice : A2021-736 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.14214/sf.10244 En ligne : https://doi.org/10.14214/sf.10244 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98695 [article]

A novel fire index-based burned area change detection approach using Landsat-8 OLI data / Sicong Liu in European journal of remote sensing, vol 53 n° 1 (2020)  

Public [article]  inEuropean journal of remote sensing > vol 53 n° 1 (2020) . - pp 104 - 112 Titre : A novel fire index-based burned area change detection approach using Landsat-8 OLI data Type de document : Article/Communication Auteurs : Sicong Liu, Auteur ; Yongjie Zheng, Auteur ; Michele Dalponte, Auteur ; Xiaohua Tong, Auteur Année de publication : 2020 Article en page(s) : pp 104 - 112 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] brûlis [Termes IGN] détection de changement [Termes IGN] image Landsat-OLI [Termes IGN] image multibande [Termes IGN] image multitemporelle [Termes IGN] incendie de forêt [Termes IGN] seuillage d'image [Termes IGN] signature spectrale Résumé : (auteur) Change detection from multi-temporal remote sensing images is an effective way to identify the burned areas after forest fires. However, the complex image scenario and the similar spectral signatures in multispectral bands may lead to many false positive errors, which make it difficult to exact the burned areas accurately. In this paper, a novel-burned area change detection approach is proposed. It is designed based on a new Normalized Burn Ratio-SWIR (NBRSWIR) index and an automatic thresholding algorithm. The effectiveness of the proposed approach is validated on three Landsat-8 data sets presenting various fire disaster events worldwide. Compared to eight index-based detection methods that developed in the literature, the proposed approach has the best performance in terms of class separability (2.49, 1.74 and 2.06) and accuracy (98.93%, 98.57% and 99.51%) in detecting the burned areas. Simultaneously, it can also better suppress the complex irrelevant changes in the background. Numéro de notice : A2020-167 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1080/22797254.2020.1738900 Date de publication en ligne : 16/03/2020 En ligne : https://doi.org/10.1080/22797254.2020.1738900 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94836 [article]

Effects of forest structure and airborne laser scanning point cloud density on 3D delineation of individual tree crowns / Kaja Kandare in European journal of remote sensing, vol 49 n° 1 (2016)  

Public [article]  inEuropean journal of remote sensing > vol 49 n° 1 (2016) . - pp 337 - 359 Titre : Effects of forest structure and airborne laser scanning point cloud density on 3D delineation of individual tree crowns Type de document : Article/Communication Auteurs : Kaja Kandare, Auteur ; Hans Ole Ørka, Auteur ; Jonathan Cheung-Wai Chan, Auteur ; Michele Dalponte, Auteur Année de publication : 2016 Article en page(s) : pp 337 - 359 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] Alpes [Termes IGN] délimitation [Termes IGN] densité des points [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] forêt alpestre [Termes IGN] houppier [Termes IGN] Italie [Termes IGN] semis de points [Termes IGN] structure d'un peuplement forestier Résumé : (auteur) This paper presents a 3D delineation method for airborne laser scanning point cloud. The method is based on an unsupervised clustering technique applied on horizontal slices followed by vertical merging based on overlapping among clusters. On an Alpine forest dataset, we analysed the effects of different forest structures and point cloud densities on tree crown delineation. Forest structure affects mainly the omission error, which eases with homogeneous tree spacing and sizes, while on the commission error forest structure has only slight effect. Delineation accuracy increases with higher point densities where Mann-Whitney-Wilcoxon test shows that accuracy differences between thinned data and original data are statistically significant. Numéro de notice : A2016-829 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.5721/EuJRS20164919 En ligne : http://dx.doi.org/10.5721/EuJRS20164919 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=82709 [article]

Semi-supervised SVM for individual tree crown species classification / Michele Dalponte in ISPRS Journal of photogrammetry and remote sensing, vol 110 (December 2015)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 110 (December 2015) . - pp 77 – 87 Titre : Semi-supervised SVM for individual tree crown species classification Type de document : Article/Communication Auteurs : Michele Dalponte, Auteur ; Levi Theodor Ene, Auteur ; Mattia Marconcini, Auteur ; et al., Auteur Année de publication : 2015 Article en page(s) : pp 77 – 87 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] classification dirigée [Termes IGN] classification par séparateurs à vaste marge [Termes IGN] données laser [Termes IGN] forêt boréale [Termes IGN] image hyperspectrale [Termes IGN] inventaire forestier local Résumé : (auteur) In this paper a novel semi-supervised SVM classifier is presented, specifically developed for tree species classification at individual tree crown (ITC) level. In ITC tree species classification, all the pixels belonging to an ITC should have the same label. This assumption is used in the learning of the proposed semi-supervised SVM classifier (ITC-S3VM). This method exploits the information contained in the unlabeled ITC samples in order to improve the classification accuracy of a standard SVM. The ITC-S3VM method can be easily implemented using freely available software libraries. The datasets used in this study include hyperspectral imagery and laser scanning data acquired over two boreal forest areas characterized by the presence of three information classes (Pine, Spruce, and Broadleaves). The experimental results quantify the effectiveness of the proposed approach, which provides classification accuracies significantly higher (from 2% to above 27%) than those obtained by the standard supervised SVM and by a state-of-the-art semi-supervised SVM (S3VM). Particularly, by reducing the number of training samples (i.e. from 100% to 25%, and from 100% to 5% for the two datasets, respectively) the proposed method still exhibits results comparable to the ones of a supervised SVM trained with the full available training set. This property of the method makes it particularly suitable for practical forest inventory applications in which collection of in situ information can be very expensive both in terms of cost and time. Numéro de notice : A2015-894 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2015.10.010 En ligne : http://dx.doi.org/10.1016/j.isprsjprs.2015.10.010 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=79445 [article]