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3D remote sensing applications in forest ecology / Hooman Latifi (2019)  

Public Titre : 3D remote sensing applications in forest ecology : Composition, structure and function Type de document : Monographie Auteurs : Hooman Latifi, Éditeur scientifique ; Rubén Valbuena, Éditeur scientifique Editeur : Bâle [Suisse] : Multidisciplinary Digital Publishing Institute MDPI Année de publication : 2019 Importance : 188 p. Format : 17 x 25 cm ISBN/ISSN/EAN : 978-3-03921-782-3 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] analyse spatio-temporelle [Termes IGN] diamètre à hauteur de poitrine [Termes IGN] données TLS (télémétrie) [Termes IGN] écologie forestière [Termes IGN] fusion de données [Termes IGN] hauteur des arbres [Termes IGN] image multibande [Termes IGN] inventaire forestier étranger (données) [Termes IGN] structure d'un peuplement forestier Résumé : (auteur) The composition, structure and function of forest ecosystems are the key features characterizing their ecological properties, and can thus be crucially shaped and changed by various biotic and abiotic factors on multiple spatial scales. The magnitude and extent of these changes in recent decades calls for enhanced mitigation and adaption measures. Remote sensing data and methods are the main complementary sources of up-to-date synoptic and objective information of forest ecology. Due to the inherent 3D nature of forest ecosystems, the analysis of 3D sources of remote sensing data is considered to be most appropriate for recreating the forest's compositional, structural and functional dynamics. In this Special Issue of Forests, we published a set of state-of-the-art scientific works including experimental studies, methodological developments and model validations, all dealing with the general topic of 3D remote sensing-assisted applications in forest ecology. We showed applications in forest ecology from a broad collection of method and sensor combinations, including fusion schemes. All in all, the studies and their focuses are as broad as a forest's ecology or the field of remote sensing and, thus, reflect the very diverse usages and directions toward which future research and practice will be directed. Note de contenu : 1- Current trends in forest ecological applications of three-dimensional remote sensing: transition from experimental to operational solutions? 2- Improving estimation accuracy of growing stock by multi-frequency SAR and multi-spectral data over Iran’s heterogeneously-structured broadleaf hyrcanian forests 3- Fractional cover mapping of invasive plant species by combining very high-resolution stereo and multi-sensor multispectral imageries 4- Relationships between satellite-based spectral burned ratios and terrestrial laser scanning 5- Mapping maximum tree height of the Great Khingan Mountain, inner Mongolia Using the allometric scaling and resource limitations model 6- Can field crews telecommute? varied data quality from citizen science tree inventories conducted using street-level imagery 7- Do high-voltage power transmission lines affect forest landscape and vegetation growth: evidence from a case for Southeastern of China 8- Mapping forest canopy height in mountainous areas using ZiYuan-3 stereo images and Landsat data 9- Application of terrestrial laser scanner to evaluate the influence of root collar geometry on stump height after mechanized forest operations 10- Sensitivity of codispersion to noise and error in ecological and environmental data 11- Estimating individual tree height and diameter at breast height (DBH) from terrestrial laser scanning (TLS) data at plot level Numéro de notice : 25931 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Monographie En ligne : https://doi.org/10.3390/books978-3-03921-783-0 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96212

Multi-scale assessment of invasive plant species diversity using Pléiades 1A, RapidEye and Landsat-8 data / Siddhartha Khare in Geocarto international, vol 33 n° 7 (July 2018)  

Public [article]  inGeocarto international > vol 33 n° 7 (July 2018) . - pp 681 - 698 Titre : Multi-scale assessment of invasive plant species diversity using Pléiades 1A, RapidEye and Landsat-8 data Type de document : Article/Communication Auteurs : Siddhartha Khare, Auteur ; Hooman Latifi, Auteur ; Sanjay Kumar Ghosh, Auteur Année de publication : 2018 Article en page(s) : pp 681 - 698 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] arbre caducifolié [Termes IGN] espèce exotique envahissante [Termes IGN] forêt [Termes IGN] Himalaya [Termes IGN] image Landsat-8 [Termes IGN] image optique [Termes IGN] image Pléiades-HR [Termes IGN] image RapidEye [Termes IGN] réflectance végétale Résumé : (Auteur) We used a full remote sensing-based approach to assess plant species diversity in large and inaccessible areas affected by Lantana camara L., a common invasive species within the deciduous forests of Western Himalayan region of India, using spectral heterogeneity information extracted from optical data. The spread of L. camara was precisely mapped by Pléiades 1A data, followed by comparing Pléiades 1A, RapidEye and Landsat-8 OLI – assessed plant species diversities in invaded areas. The single plant species analysis was improved by Pléiades 1A-based diversity analysis, and higher species diversity values were observed for mixed vegetation cover. Furthermore, lower Coefficient of Variation and Renyi diversity values were observed where L. camara was the only species, while higher variations were observed in areas with a mixed spectral reflectance. This study was concluded to add a crucial baseline to the previous studies on remote sensing-based solutions for rapid estimation of biodiversity attributes. Numéro de notice : A2018-334 Affiliation des auteurs : non IGN Thématique : BIODIVERSITE/FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2017.1289562 Date de publication en ligne : 10/02/2017 En ligne : https://doi.org/10.1080/10106049.2017.1289562 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=90530 [article]

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Multi-model estimation of understorey shrub, herb and moss cover in temperate forest stands by laser scanner data / Hooman Latifi in Forestry, an international journal of forest research, vol 90 n° 4 (October 2017)  

Public [article]  inForestry, an international journal of forest research > vol 90 n° 4 (October 2017) . - pp 496 - 514 Titre : Multi-model estimation of understorey shrub, herb and moss cover in temperate forest stands by laser scanner data Type de document : Article/Communication Auteurs : Hooman Latifi, Auteur ; Steven Hill, Auteur ; Bastian Schumann, Auteur ; Marco Heurich, Auteur ; Stefan Dech, Auteur Année de publication : 2017 Article en page(s) : pp 496 - 514 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] classification par forêts d'arbres décisionnels [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] estimation statistique [Termes IGN] forêt tempérée [Termes IGN] habitat forestier [Termes IGN] sous-bois Résumé : (Auteur) In temperate forests, the highest plant richness is regularly found in the understorey, i.e. shrub, tree regeneration, herbal and moss covers, which provides important food and shelter for other plant and animal species. Here, Light Detection And Ranging (LiDAR) remote sensing was investigated as a surrogate to laborious field surveys to improve understanding of the causal and predictive attributes of understorey. We designed a study in which we used a high-density LiDAR point cloud and applied a thinning algorithm to simulate two lower density point clouds including first and last returns and half of the remaining points (half-thinned data) and only first and last returns (F/L-thinned data). From each dataset, several over- and understorey-related statistical metrics were derived. Each of the three sets of LiDAR metrics was then combined with the forest habitat information to estimate the recorded proportions of shrub, herb and moss coverages. We used three different model procedures including zero-and-one-inflated beta regression (ZOINBR), ordinary least squares with logit-transformed response variables (logistic model) and a machine learning random forest (RF) method. The logistic and ZOINBR model results showed highly significant relationships between LiDAR metrics and habitat types in explaining understorey coverage. The highest coefficients of determination included r2 = 0.80 for shrub cover (estimated by F/L-thinned data and ZOINBR model), r2 = 0.53 for herb cover (estimated by half-thinned data and logistic model) and r2 = 0.48 for moss cover (estimated by half-thinned data and logistic model). RF models returned the best predictive performances (i.e. the lowest root mean square errors). Despite slight differences, no substantial difference was observed amongst the performances achieved by the original, half-thinned and F/L-thinned point clouds. Moreover, the ZOINBR models did not improve predictive performances compared with the logistic model, which suggests that the latter should be preferred due to its greater simplicity and parsimony. Despite the differences between our simulated data and the real-world LiDAR point clouds of different point densities, the results of this study are thought to mostly reflect how LiDAR and forest habitat data can be combined for deriving ecologically relevant information on temperate forest understorey vegetation layers. This, in turn, increases the applicability of prediction results for overarching aims such as forest and wildlife management. Numéro de notice : A2017-906 Affiliation des auteurs : non IGN Thématique : FORET/MATHEMATIQUE Nature : Article DOI : 10.1093/forestry/cpw066 Date de publication en ligne : 27/01/2017 En ligne : https://doi.org/10.1093/forestry/cpw066 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93195 [article]

Estimating over- and understorey canopy density of temperate mixed stands by airborne LiDAR data / Hooman Latifi in Forestry, an international journal of forest research, vol 89 n° 1 (January 2016)  

Public [article]  inForestry, an international journal of forest research > vol 89 n° 1 (January 2016) . - pp 61 - 81 Titre : Estimating over- and understorey canopy density of temperate mixed stands by airborne LiDAR data Type de document : Article/Communication Auteurs : Hooman Latifi, Auteur ; Marco Heurich, Auteur ; Florian Hartig, Auteur ; Jorg Müller, Auteur ; et al., Auteur Année de publication : 2016 Article en page(s) : pp 61 - 81 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] Abies alba [Termes IGN] Acer pseudoplatanus [Termes IGN] Betula pendula [Termes IGN] betula pubescens [Termes IGN] canopée [Termes IGN] classification par forêts d'arbres décisionnels [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] Fagus sylvatica [Termes IGN] habitat forestier [Termes IGN] Picea abies [Termes IGN] régression [Termes IGN] sorbus aucuparia [Termes IGN] sous-bois Résumé : (auteur) Estimating forest structural attributes is one of the essential forestry-related remote sensing applications. The methods applied so far typically concentrate on the structure of the overstorey. For various conservation and management applications, however, information on lower layers is also of great interest. Detecting understorey cover by remote sensing is challenging, as passive sensors do not penetrate to the forest ground layer. An alternative to these is 3D metrics from active light detection and ranging (LiDAR). Here, we evaluate this technique for describing the vegetation density of multiple stand layers within the temperate stands of a large protected area in south-eastern Germany. We combined LiDAR metrics and information on forest habitat types with regression models to investigate LiDAR metrics that are significantly correlated with vegetation density. The top canopy and the herbal layer showed strong correlations with the applied LiDAR metrics, whereas the predictive power was lower for the intermediate stand layers. Moreover, our results suggest that the relationship between LiDAR predictors and vegetation density depends on the forest type. A comparison of the regression models with random forest predictions showed no major improvement in predictive error. In conclusion, this study highlights the value of the LiDAR metrics for characterizing the structural properties of lower forest layers, which has implications for wildlife and forest management applications, especially in protected areas. Numéro de notice : A2016--102 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.1093/forestry/cpv032 En ligne : https://doi.org/10.1093/forestry/cpv032 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=84668 [article]