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Individual tree diameter growth modeling system for Dalat pine (Pinus dalatensis Ferré) of the upland mixed tropical forests / Bao Huy in Forest ecology and management, vol 480 (15 January 2021)  

Public [article]  inForest ecology and management > vol 480 (15 January 2021) . - n° 118612 Titre : Individual tree diameter growth modeling system for Dalat pine (Pinus dalatensis Ferré) of the upland mixed tropical forests Type de document : Article/Communication Auteurs : Bao Huy, Auteur ; Le Canh nam, Auteur ; Krishna P. Poudel, Auteur ; Hailemariam Temesgen, Auteur Année de publication : 2021 Article en page(s) : n° 118612 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Végétation [Termes descripteurs IGN] cerne [Termes descripteurs IGN] conservation de la flore [Termes descripteurs IGN] croissance végétale [Termes descripteurs IGN] diamètre à hauteur de poitrine [Termes descripteurs IGN] facteur édaphique [Termes descripteurs IGN] flore endémique [Termes descripteurs IGN] forêt tropicale [Termes descripteurs IGN] modèle de croissance [Termes descripteurs IGN] Pinus (genre) [Termes descripteurs IGN] Viet Nam Résumé : (auteur) Pinus dalatensis Ferré (Dalat pine, or five-needle pine, locally) is an endemic large tree species of Vietnam that has both high timber and non-timber values. It is also a rare tree species listed in the Red List of the International Union for Conservation of Nature (IUCN). The objective of this study was to develop an individual tree diameter growth modeling system to facilitate the sustainable management and conservation of this species. We used Haglöf Sweden ® increment borers to collect tree ring samples from a total of 56 trees resulting in a dataset of 4566 diameter at breast height (dbh, cm) measurements at age (t, year) and obtained the associated ecological environmental factors in three different sites in the Central Highlands, Vietnam. A subset of this dataset (n = 1264) also had the climate data collected over the period of past 32–38 years (from 1980 to 2011 and from 1979 to 2016). Weighted mixed-effects models were used to model Dalat pine trees growth and account for autocorrelation and heteroscedasticity of the dbh measurements. Cross validation over 200 realizations were used to select the best equation form of dbh growth and incorporate the environmental effects and climatic factors that help improve reliability of the models. Under the mixed-effects modeling paradigm, the Mitscherlich equation fitted with random effects of ecological environmental factors (eco-subregions and altitude) and climatic factors (temperature, humidity, and temperature in dry and in rainy seasons) produced the best results. Whereas, under the fixed-effect modeling paradigm, the models that used the exponential function of environmental or climatic factors as the modifiers of an average diameter growth performed the best (Bias = −5.9% and RMSE = 10.0 cm). The models developed in this study will be useful for forecasting growth and for silvicultural planning under shifting environment and climate and are expected to contribute to the sustainable management of this endemic species. Numéro de notice : A2021-063 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.foreco.2020.118612 date de publication en ligne : 08/10/2020 En ligne : https://doi.org/10.1016/j.foreco.2020.118612 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96743 [article]

Mapping tree species deciduousness of tropical dry forests combining reflectance, spectral unmixing, and texture data from high-resolution imagery / Astrid Helena Huechacona-Ruiz in Forests, vol 11 n°11 (November 2020)  

Public [article]  inForests > vol 11 n°11 (November 2020) . - n° 1234 Titre : Mapping tree species deciduousness of tropical dry forests combining reflectance, spectral unmixing, and texture data from high-resolution imagery Type de document : Article/Communication Auteurs : Astrid Helena Huechacona-Ruiz, Auteur ; Juan Manuel Dupuy, Auteur ; Naomi B. Schwartz, Auteur Année de publication : 2020 Article en page(s) : n° 1234 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes descripteurs IGN] analyse des mélanges spectraux [Termes descripteurs IGN] arbre caducifolié [Termes descripteurs IGN] carte de la végétation [Termes descripteurs IGN] classification par forêts aléatoires [Termes descripteurs IGN] distribution spatiale [Termes descripteurs IGN] forêt tropicale [Termes descripteurs IGN] image proche infrarouge [Termes descripteurs IGN] image Sentinel-MSI [Termes descripteurs IGN] Normalized Difference Vegetation Index [Termes descripteurs IGN] réflectance [Termes descripteurs IGN] texture d'image [Termes descripteurs IGN] Yucatan Résumé : (auteur) In tropical dry forests, deciduousness (i.e., leaf shedding during the dry season) is an important adaptation of plants to cope with water limitation, which helps trees adjust to seasonal drought. Deciduousness is also a critical factor determining the timing and duration of carbon fixation rates, and affecting energy, water, and carbon balance. Therefore, quantifying deciduousness is vital to understand important ecosystem processes in tropical dry forests. The aim of this study was to map tree species deciduousness in three types of tropical dry forests along a precipitation gradient in the Yucatan Peninsula using Sentinel-2 imagery. We propose an approach that combines reflectance of visible and near-infrared bands, normalized difference vegetation index (NDVI), spectral unmixing deciduous fraction, and several texture metrics to estimate the spatial distribution of tree species deciduousness. Deciduousness in the study area was highly variable and decreased along the precipitation gradient, while the spatial variation in deciduousness among sites followed an inverse pattern, ranging from 91.5 to 43.3% and from 3.4 to 9.4% respectively from the northwest to the southeast of the peninsula. Most of the variation in deciduousness was predicted jointly by spectral variables and texture metrics, but texture metrics had a higher exclusive contribution. Moreover, including texture metrics as independent variables increased the variance of deciduousness explained by the models from R2 = 0.56 to R2 = 0.60 and the root mean square error (RMSE) was reduced from 16.9% to 16.2%. We present the first spatially continuous deciduousness map of the three most important vegetation types in the Yucatan Peninsula using high-resolution imagery. Numéro de notice : A2020-756 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/f11111234 date de publication en ligne : 23/11/2020 En ligne : https://doi.org/10.3390/f11111234 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96468 [article]

Wide-area near-real-time monitoring of tropical forest degradation and deforestation using Sentinel-1 / Dirk Hoekman in Remote sensing, vol 12 n° 19 (October 2020)  

Public [article]  inRemote sensing > vol 12 n° 19 (October 2020) . - 32 p. Titre : Wide-area near-real-time monitoring of tropical forest degradation and deforestation using Sentinel-1 Type de document : Article/Communication Auteurs : Dirk Hoekman, Auteur ; Boris Kooij, Auteur ; Marcela J. Quiñones, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : 32 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes descripteurs IGN] Amazonie [Termes descripteurs IGN] Bornéo, île de [Termes descripteurs IGN] déboisement [Termes descripteurs IGN] dégradation de l'environnement [Termes descripteurs IGN] détection de changement [Termes descripteurs IGN] forêt tropicale [Termes descripteurs IGN] image radar [Termes descripteurs IGN] image Sentinel-SAR [Termes descripteurs IGN] image TerraSAR-X [Termes descripteurs IGN] modèle physique [Termes descripteurs IGN] série temporelle [Termes descripteurs IGN] surveillance forestière [Termes descripteurs IGN] tourbière Résumé : (auteur) The use of Sentinel-1 (S1) radar for wide-area, near-real-time (NRT) tropical-forest-change monitoring is discussed, with particular attention to forest degradation and deforestation. Since forest change can relate to processes ranging from high-impact, large-scale conversion to low-impact, selective logging, and can occur in sites having variable topographic and environmental properties such as mountain slopes and wetlands, a single approach is insufficient. The system introduced here combines time-series analysis of small objects identified in S1 data, i.e., segments containing linear features and apparent small-scale disturbances. A physical model is introduced for quantifying the size of small (upper-) canopy gaps. Deforestation detection was evaluated for several forest landscapes in the Amazon and Borneo. Using the default system settings, the false alarm rate (FAR) is very low (less than 1%), and the missed detection rate (MDR) varies between 1.9% ± 1.1% and 18.6% ± 1.0% (90% confidence level). For peatland landscapes, short radar detection delays up to several weeks due to high levels of soil moisture may occur, while, in comparison, for optical systems, detection delays up to 10 months were found due to cloud cover. In peat swamp forests, narrow linear canopy gaps (road and canal systems) could be detected with an overall accuracy of 85.5%, including many gaps barely visible on hi-res SPOT-6/7 images, which were used for validation. Compared to optical data, subtle degradation signals are easier to detect and are not quickly lost over time due to fast re-vegetation. Although it is possible to estimate an effective forest-cover loss, for example, due to selective logging, and results are spatiotemporally consistent with Sentinel-2 and TerraSAR-X reference data, quantitative validation without extensive field data and/or large hi-res radar datasets, such as TerraSAR-X, remains a challenge. Numéro de notice : A2020-633 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.3390/rs12193263 date de publication en ligne : 08/10/2020 En ligne : https://doi.org/10.3390/rs12193263 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96056 [article]

Evaluating the impact of declining tsetse fly (Glossina pallidipes) habitat in the Zambezi valley of Zimbabwe / Farai Matawa in Geocarto international, vol 35 n° 12 ([01/09/2020])  

Public [article]  inGeocarto international > vol 35 n° 12 [01/09/2020] . - pp 1373 - 1384 Titre : Evaluating the impact of declining tsetse fly (Glossina pallidipes) habitat in the Zambezi valley of Zimbabwe Type de document : Article/Communication Auteurs : Farai Matawa, Auteur ; Amon Murwira, Auteur ; Peter M. Atkinson, Auteur Année de publication : 2020 Article en page(s) : pp 1373 - 1384 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes descripteurs IGN] biodiversité [Termes descripteurs IGN] bovin [Termes descripteurs IGN] couvert végétal [Termes descripteurs IGN] diptère [Termes descripteurs IGN] distance [Termes descripteurs IGN] distribution spatiale [Termes descripteurs IGN] forêt tropicale [Termes descripteurs IGN] habitat d'espèce [Termes descripteurs IGN] maladie parasitaire [Termes descripteurs IGN] Zambèze (fleuve) [Termes descripteurs IGN] Zimbabwe Résumé : (auteur) Tsetse flies transmit trypanosomes that cause Human African Trypanosomiasis (HAT) in humans and African Animal Trypanosomiasis (AAT) in animals. Understanding historical trends in the spatial distribution of tsetse fly habitat is necessary for planning vector control measures. The objectives of this study were (i) to test for evidence of any trends in suitable tsetse fly habitat and (ii) to test whether there is an association between trypanosomiasis detected from livestock sampled in dip tanks and local tsetse habitat in the project area. Results indicate a significant decreasing trend in the amount of suitable habitat. There is no significant correlation between trypanosomiasis prevalence rates in cattle and distance from patches of suitable tsetse habitat. The observed low trypanosomiasis prevalence and the lack of dependence on suitable tsetse fly habitat can be explained by the observed decreases in suitable tsetse habitat, which themselves are due to expansion of settlement and agriculture in North Western Zimbabwe. Numéro de notice : A2020-486 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2019.1576780 date de publication en ligne : 21/03/2019 En ligne : https://doi.org/10.1080/10106049.2019.1576780 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95653 [article]

Improved supervised learning-based approach for leaf and wood classification from LiDAR point clouds of forests / Sruthi M. Krishna Moorthy in IEEE Transactions on geoscience and remote sensing, vol 58 n° 5 (May 2020)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 58 n° 5 (May 2020) . - pp 3057 - 3070 Titre : Improved supervised learning-based approach for leaf and wood classification from LiDAR point clouds of forests Type de document : Article/Communication Auteurs : Sruthi M. Krishna Moorthy, Auteur ; Kim Calders, Auteur ; Matheus B. Vicari, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 3057 - 3070 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes descripteurs IGN] apprentissage automatique [Termes descripteurs IGN] apprentissage dirigé [Termes descripteurs IGN] atmosphère terrestre [Termes descripteurs IGN] canopée [Termes descripteurs IGN] classification dirigée [Termes descripteurs IGN] classification par forêts aléatoires [Termes descripteurs IGN] données lidar [Termes descripteurs IGN] faisceau laser [Termes descripteurs IGN] feuille (végétation) [Termes descripteurs IGN] foresterie [Termes descripteurs IGN] forêt de feuillus [Termes descripteurs IGN] forêt tropicale [Termes descripteurs IGN] plus proche voisin (algorithme) [Termes descripteurs IGN] précision de la classification [Termes descripteurs IGN] Python (langage de programmation) [Termes descripteurs IGN] semis de points [Termes descripteurs IGN] transfert radiatif Résumé : (auteur) Accurately classifying 3-D point clouds into woody and leafy components has been an interest for applications in forestry and ecology including the better understanding of radiation transfer between canopy and atmosphere. The past decade has seen an increase in the methods attempting to classify leaves and wood in point clouds based on radiometric or geometric features. However, classification purely based on radiometric features is sensor-specific, and the method by which the local neighborhood of a point is defined affects the accuracy of classification based on geometric features. Here, we present a leaf-wood classification method combining geometrical features defined by radially bounded nearest neighbors at multiple spatial scales in a machine learning model. We compared the performance of three different machine learning models generated by the random forest (RF), XGBoost, and lightGBM algorithms. Using multiple spatial scales eliminates the need for an optimal neighborhood size selection and defining the local neighborhood by radially bounded nearest neighbors makes the method broadly applicable for point clouds of varying quality. We assessed the model performance at the individual tree- and plot-level on field data from tropical and deciduous forests, as well as on simulated point clouds. The method has an overall average accuracy of 94.2% on our data sets. For other data sets, the presented method outperformed the methods in literature in most cases without the need for additional postprocessing steps that are needed in most of the existing methods. We provide the entire framework as an open-source python package. Numéro de notice : A2020-232 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2019.2947198 date de publication en ligne : 31/10/2019 En ligne : https://doi.org/10.1109/TGRS.2019.2947198 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94970 [article]

Warming effects on morphological and physiological performances of four subtropical montane tree species Authors Authors and affiliations / Yiyong Li in Annals of Forest Science [en ligne], Vol 77 n° 1 (March 2020)  

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Can Carbon Sequestration in Tasmanian “Wet” Eucalypt Forests Be Used to Mitigate Climate Change? Forest Succession, the Buffering Effects of Soils, and Landscape Processes Must Be Taken into Account / Peter D. McIntosh in International journal of forestry research, vol 2020 ([01/02/2020])  

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Individual tree crown segmentation in tropical peat swamp forest using airborne hyperspectral data / Sitinor Atikah Nordin in Geocarto international, vol 34 n° 11 ([15/08/2019])  

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Monitoring the structure of forest restoration plantations with a drone-lidar system / D.R.A. Almeida in International journal of applied Earth observation and geoinformation, vol 79 (July 2019)  

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Object-based random forest modelling of aboveground forest biomass outperforms a pixel-based approach in a heterogeneous and mountain tropical environment / Eduarda M.O. Silveira in International journal of applied Earth observation and geoinformation, vol 78 (June 2019)  

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Estimating architecture-based metabolic scaling exponents of tropical trees using terrestrial LiDAR and 3D modelling / Alvaro Lau in Forest ecology and management, vol 439 (1 May 2019)  

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Calibration of the normalized radar cross section for sentinel-1 wave mode / Huimin Li in IEEE Transactions on geoscience and remote sensing, vol 57 n° 3 (March 2019)  

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Estimation of aboveground biomass and carbon in a tropical rain forest in Gabon using remote sensing and GPS data / Kalifa Goïta in Geocarto international, vol 34 n° 3 ([01/03/2019])  

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Forest degradation and biomass loss along the Chocó region of Colombia / Victoria Meyer in Carbon Balance and Management, vol 14 (March 2019)  

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Tree species classification in tropical forests using visible to shortwave infrared WorldView-3 images and texture analysis / Matheus Pinheiro Ferreira in ISPRS Journal of photogrammetry and remote sensing, vol 149 (March 2019)  

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