Centre dedocumentation
scientifique

Mitigating the risk of wind damage at the forest landscape level by using stand neighbourhood and terrain elevation information in forest planning / Roope Ruotsalainen in Forestry, an international journal of forest research, vol 96 n° 1 (January 2023)  

Public [article]  inForestry, an international journal of forest research > vol 96 n° 1 (January 2023) . - pp 121 - 134 Titre : Mitigating the risk of wind damage at the forest landscape level by using stand neighbourhood and terrain elevation information in forest planning Type de document : Article/Communication Auteurs : Roope Ruotsalainen, Auteur ; Timo Pukkala, Auteur ; Veli-Pekka Ikonen, Auteur Année de publication : 2023 Article en page(s) : pp 121 - 134 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] altitude [Termes IGN] canopée [Termes IGN] dommage forestier causé par facteurs naturels [Termes IGN] Finlande [Termes IGN] forêt boréale [Termes IGN] gestion forestière [Termes IGN] inventaire forestier étranger (données) [Termes IGN] modèle numérique de surface [Termes IGN] pondération [Termes IGN] prévention des risques [Termes IGN] topographie locale [Termes IGN] vent [Termes IGN] voisinage (relation topologique) [Vedettes matières IGN] Foresterie Résumé : (auteur) Wind damage and the bark beetle outbreaks associated with it are major threats to non-declining, long-term wood production in boreal forests. We studied whether the risk of wind damage in a forested landscape could be decreased by using stand neighbourhood information in conjunction with terrain elevation information. A reference management plan minimized the differences in canopy height at stand boundaries and did not utilize information on the topography of the terrain, overlooking the possibility that the risk of windthrow may depend on the elevation of the terrain. Alternative management plans were developed by using four different weighting schemes when minimizing differences in canopy height at stand boundaries: (1) no weight (reference); (2) mean terrain elevation at the stand boundary; (3) deviation of the mean elevation of the boundary from the mean elevation of the terrain within a 100-m radius and (4) multipliers that described the effect of topography on wind speed at the stand boundary. For each management plan, we calculated the total number of at-risk trees and the total area of vulnerable stand edge. These statistics were based on the calculated critical wind speeds needed to uproot trees in stand edge zones. Minimization of the weighted mean of canopy height differences between adjacent stands resulted in homogeneous landscapes in terms of canopy height. Continuous cover management was often preferred instead of rotation management due to smaller canopy height differences between adjacent stands and its economical superiority. The best weighting scheme for calculating the mean canopy height difference between adjacent stands was the deviation between the mean elevation of the boundary and the mean elevation of the terrain within 100 m of the boundary. However, the differences between the weighting schemes were small. It was found that reasonably simple methods, based on a digital terrain model, a stand map, and the canopy heights of stands, could be used in forest planning to minimize the risk of wind damage. Validation against actual wind damages is required to assess the reliability of the results and to further develop the methodology presented. Numéro de notice : A2023-114 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article DOI : 10.1093/forestry/cpac039 Date de publication en ligne : 08/10/2022 En ligne : https://doi.org/10.1093/forestry/cpac039 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102481 [article]

Beyond topo-climatic predictors: Does habitats distribution and remote sensing information improve predictions of species distribution models? / Arthur Sanguet in Global ecology and conservation, vol 39 (November 2022)  

Public [article]  inGlobal ecology and conservation > vol 39 (November 2022) . - n° e02286 Titre : Beyond topo-climatic predictors: Does habitats distribution and remote sensing information improve predictions of species distribution models? Type de document : Article/Communication Auteurs : Arthur Sanguet, Auteur ; Nicolas Wyler, Auteur ; Blaise Petitpierre, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° e02286 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] carte d'occupation du sol [Termes IGN] changement climatique [Termes IGN] distribution spatiale [Termes IGN] échantillonnage de données [Termes IGN] habitat (nature) [Termes IGN] modèle de simulation [Termes IGN] montagne [Termes IGN] pédologie locale [Termes IGN] Suisse [Termes IGN] télédétection [Termes IGN] topographie locale [Termes IGN] zone humide [Vedettes matières IGN] Ecologie forestière Résumé : (auteur) Species Distribution Models (SDM) represent a powerful tool to predict species’ habitat suitability on a landscape and fill the gap between truncated observation data and all possible locations. SDMs have been widely used in theoretical studies of species niches as well as in conservation applications. Here, we evaluated the impacts of predictors’ type on models’ performances and spatial predictions using 72 plant species belonging to six ecological groups at a regional scale in the area of Geneva (Switzerland). Twelve models were created using various combinations of high-resolution (25 m) explanatory variables including topography, pedology, climate, habitats and remote sensing data. Models integrating a combination of habitats and topopedo-climatic predictors had significantly higher performances, while remote sensing predictors showed low performances. Our results suggest that the number and the level of details of habitat predictors (broad or very precise) do not fundamentally affect prediction maps. However, selecting too few, overly simplified or exceedingly complex habitat predictors tend to lower models’ performances. The use of eight habitat categories complemented with eight topopedo-climatic predictors produced models with the highest performances. Ecological groups of species responded differently to models and while alpine and ruderal species have greater average performances due to a high affinity with topopedo-climatic predictors, wetlands’ species were less performant on average. These results underline the necessity of developing or having access to habitats distribution data especially in a conservation context. Numéro de notice : A2022-815 Affiliation des auteurs : non IGN Thématique : BIODIVERSITE/FORET Nature : Article DOI : 10.1016/j.gecco.2022.e02286 Date de publication en ligne : 13/09/2022 En ligne : https://doi.org/10.1016/j.gecco.2022.e02286 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101977 [article]

Modelling and accessing land degradation vulnerability using remote sensing techniques and the analytical hierarchy process approach / Abebe Debele Tolche in Geocarto international, vol 37 n° 24 ([20/10/2022])  

Public [article]  inGeocarto international > vol 37 n° 24 [20/10/2022] . - pp 7122 - 7142 Titre : Modelling and accessing land degradation vulnerability using remote sensing techniques and the analytical hierarchy process approach Type de document : Article/Communication Auteurs : Abebe Debele Tolche, Auteur ; Megersa Adugna Gurara, Auteur ; Quoc Bao Pham, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 7122 - 7142 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] carte thématique [Termes IGN] dégradation des sols [Termes IGN] Ethiopie [Termes IGN] Google Earth [Termes IGN] image Terra-MODIS [Termes IGN] Normalized Difference Vegetation Index [Termes IGN] pédologie locale [Termes IGN] précipitation [Termes IGN] processus de hiérarchisation analytique [Termes IGN] température au sol [Termes IGN] topographie locale [Termes IGN] vulnérabilité Résumé : (auteur) Land degradation and desertification have recently become a critical problem in Ethiopia. Accordingly, identification of land degradation vulnerable zonation and mapping was conducted in Wabe Shebele River Basin, Ethiopia. Precipitation derived from Global Precipitation Measurement Mission (GMP), the Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized difference vegetation index (NDVI) and land surface temperature (LST), topography (slope), and pedological properties (i.e., soil depth, soil pH, soil texture, and soil drainage) were used in the current study. NDVI has been considered as the most significant parameter followed by the slope, precipitation and temperature. Geospatial techniques and the Analytical Hierarchy Process (AHP) approach were used to model the land degradation vulnerable index. Validation of the results with google earth image shows the applicability of the model in the study. The result is classified into very highly vulnerable (17.06%), highly vulnerable (15.01%), moderately vulnerable (32.72%), slightly vulnerable (16.40%), and very slightly vulnerable (18.81%) to land degradation. Due to the small rate of precipitation which is vulnerable to evaporation by high temperature in the region, the downstream section of the basis is categorized as highly vulnerable to Land Degradation (LD) and vice versa in the upstream section of the basin. Moreover, the validation using the Receiver Operating Characteristic (ROC) curve analysis shows an area under the ROC curve value of 80.92% which approves the prediction accuracy of the AHP method in assessing and modelling LD vulnerability zone in the study area. The study provides a substantial understanding of the effect of land degradation on sustainable land use management and development in the basin. Numéro de notice : A2022-776 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2021.1959656 Date de publication en ligne : 01/09/2021 En ligne : https://doi.org/10.1080/10106049.2021.1959656 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101831 [article]

Multi‑constellation GNSS interferometric reflectometry for the correction of long-term snow height retrieval on sloping topography / Wei Zhou in GPS solutions, vol 26 n° 4 (October 2022)  

Public [article]  inGPS solutions > vol 26 n° 4 (October 2022) . - n° 140 Titre : Multi‑constellation GNSS interferometric reflectometry for the correction of long-term snow height retrieval on sloping topography Type de document : Article/Communication Auteurs : Wei Zhou, Auteur ; Liangke Huang, Auteur ; Bing Ji, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 140 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] hauteur (coordonnée) [Termes IGN] manteau neigeux [Termes IGN] pente [Termes IGN] Ransac (algorithme) [Termes IGN] rapport signal sur bruit [Termes IGN] réflectométrie par GNSS [Termes IGN] signal GNSS [Termes IGN] système de référence altimétrique [Termes IGN] topographie locale [Termes IGN] transformation en ondelettes [Termes IGN] valeur aberrante [Vedettes matières IGN] Altimétrie Résumé : (auteur) Snow is a key parameter for global climate and hydrological systems. Global Navigation Satellite System interferometric reflectometry (GNSS-IR) has been applied to accurately monitor snow height (SH) with low cost and high temporal–spatial resolution. We proposed an improved GNSS-IR method using detrended signal-to-noise ratio (δSNR) arcs corresponding to multipath reflection tracks with different azimuths. After using wavelet decomposition and random sample consensus, noise with various frequencies for SNR arcs and outliers of reflector height (RH) estimations have been sequentially mitigated to enhance the availability of the proposed method. Thus, a height datum based on the ground RHs retrieved from multi-GNSS SNR data is established to compensate for the influence of topography variation with different azimuths in SH retrieval. The approximately 3-month δSNR datasets collected from three stations deployed on sloping topography were used to retrieve SH and compared with the existing method and in situ measurements. The results show that the root mean square errors of the retrievals derived from the proposed method for the three sites are between 4 and 8 cm, and the corresponding correlation surpasses 0.95 when compared to the reference SH datasets. Additionally, we compare the performance of a retrieval with the existing GNSS-IR Web App, and it shows an improvement in RMSE of about 7 cm. Furthermore, because topography variation has been considered, the average correction of SH retrievals is between 2 and 4 cm. The solution with the proposed method helps develop the applications of the GNSS-IR technique on complex topography. Numéro de notice : A2022-712 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article DOI : 10.1007/s10291-022-01333-0 Date de publication en ligne : 15/09/2022 En ligne : https://doi.org/10.1007/s10291-022-01333-0 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101590 [article]

Forest tree species classification based on Sentinel-2 images and auxiliary data / Haotian You in Forests, vol 13 n° 9 (september 2022)  

Public [article]  inForests > vol 13 n° 9 (september 2022) . - n° 1416 Titre : Forest tree species classification based on Sentinel-2 images and auxiliary data Type de document : Article/Communication Auteurs : Haotian You, Auteur ; Yuanwei Huang, Auteur ; Zhigang Qin, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 1416 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] classification et arbre de régression [Termes IGN] classification par forêts d'arbres décisionnels [Termes IGN] classification par séparateurs à vaste marge [Termes IGN] dioxyde d'azote [Termes IGN] distribution spatiale [Termes IGN] Extreme Gradient Machine [Termes IGN] image Sentinel-MSI [Termes IGN] phénologie [Termes IGN] précipitation [Termes IGN] réflectance spectrale [Termes IGN] température de l'air [Termes IGN] texture du sol [Termes IGN] topographie locale Résumé : (auteur) Most research on forest tree species classification based on optical image data uses information such as spectral reflectance, vegetation index, texture, and phenology data. However, owing to the limited spectral resolution of multispectral images and the high cost of hyperspectral data, there is room for improvement in the classification of tree species in large areas based on optical images. The combined application of multispectral images and other auxiliary data can provide a new method for improving tree species classification accuracy. Hence, Sentinel-2 images were used to extract spectral reflectance, spectral index, texture, and phenological information. Data for topography, precipitation, air temperature, ultraviolet aerosol index, NO2 concentration, and other variables were included as auxiliary data. Models for forest tree species classification were constructed through feature combination and feature optimization using the random forest (RF), gradient tree boost (GTB), support vector machine (SVM), and classification and regression tree (CART) algorithms. The classification results of 16 feature combinations with the 4 classification methods were compared, and the contributions of different features to the classification models of forest tree species were evaluated. Finally, the optimal classification model was selected to identify the spatial distribution of forest tree species in the study area. The model based on feature optimization gave the best results among the 16 feature combination models. The overall accuracy and kappa coefficient were increased by 18% and 0.21, respectively, compared with the spectral classification model, and by 17% and 0.20, respectively, compared with the spectral and spectral index classification model. By analyzing the feature optimization model, it was found that terrain, ultraviolet aerosol index, and phenological information ranked as the top three features in terms of importance. Although the importance of spectral reflectance and spectral index features was lower, the number of feature variables accounted for a large proportion of the total. The importance of commonly used texture features was limited, and these features were not present in the feature optimization model. The RF algorithm had the highest classification accuracy, with an overall accuracy of 82.69% and a kappa coefficient of 0.80, among the four classification algorithms. The results of GTB were close to those of RF, and the difference in overall classification accuracy was only 0.14%. However, the results of the SVM and CART algorithms were relatively weaker, with overall classification accuracies of about 70%. It can be concluded that the combined application of Sentinel-2 images and auxiliary data can improve forest tree species classification accuracy. The model based on feature optimization achieved the highest classification accuracy among the 16 feature combination models. The spectral reflectance and spectral index data extracted from optical images are useful for tree species classification, but the effect of texture features was very limited. Auxiliary data, such as topographic features, ultraviolet aerosol index, phenological features, NO2 concentration features, topographic diversity features, precipitation features, temperature features, and multi-scale topographic location index data, can effectively improve forest tree species classification accuracy. The RF algorithm had the highest accuracy, and it can be used for tree species classification space distribution identification. The combined application of Sentinel-2 images and auxiliary data can improve classification accuracy, but the highest accuracy of the model was only 82.69%, which leaves room for improvement. Thus, more effective auxiliary data and the vertical structural parameters extracted from satellite LiDAR can be combined with multispectral images to improve forest tree species classification accuracy in future research. Numéro de notice : A2022-754 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.3390/f13091416 Date de publication en ligne : 02/09/2022 En ligne : https://doi.org/10.3390/f13091416 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101757 [article]

Spatial patterns of living and dead small trees in subalpine Norway spruce forest reserves in Switzerland / Eva Bianchi in Forest ecology and management, vol 494 (August-15 2021)  

Permalink

Topoclimatic zoning of continental Chile / Donna Cortez in Journal of maps, vol 17 n° 2 (February 2021)  

Permalink

Using Sentinel-2 images to estimate topography, tidal-stage lags and exposure periods over large intertidal areas / José P. Granadeiro in Remote sensing, Vol 13 n° 2 (January-2 2021)  

Permalink

An integration of bioclimatic, soil, and topographic indicators for viticulture suitability using multi-criteria evaluation: a case study in the Western slopes of Jabal Al Arab—Syria / Karam Alsafadi in Geocarto international, vol 35 n° 13 ([01/10/2020])  

Permalink

The effect of topography and elevation on viewsheds in mountain landscapes using geovisualization / Loukas-Moysis Misthos in International journal of cartography, vol 5 n° 1 (March 2019)  

Permalink

Using LiDAR to develop high-resolution reference models of forest structure and spatial pattern / Haley L. Wiggins in Forest ecology and management, vol 434 (28 February 2019)  

Permalink

Safe separation distance score : a new metric for evaluating wildland firefighter safety zones using lidar / Michael J. Campbell in International journal of geographical information science IJGIS, vol 31 n° 7-8 (July - August 2017)  

Permalink

Les cartographes du désert / Paul Courbon in XYZ, n° 151 (juin - août 2017) 

Permalink

Exposure-related forest-steppe: A diverse landscape type determined by topography and climate / Martin Hais in Journal of Arid Environments, vol 135 (December 2016)  

Permalink

Generalized terrain topography in radar scattering models / Mariko S. Burgin in IEEE Transactions on geoscience and remote sensing, vol 54 n° 7 (July 2016)  

Permalink