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Spatial assessment of ecosystem services provisioning changes in a forest-dominated protected area in NE Turkey / Can Vatandaslar in Environmental Monitoring and Assessment, vol 194 n° 8 (August 2022)  

Public [article]  inEnvironmental Monitoring and Assessment > vol 194 n° 8 (August 2022) . - n° 539 Titre : Spatial assessment of ecosystem services provisioning changes in a forest-dominated protected area in NE Turkey Type de document : Article/Communication Auteurs : Can Vatandaslar, Auteur Année de publication : 2022 Article en page(s) : n° 539 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] analyse spatio-temporelle [Termes IGN] parc naturel national [Termes IGN] protection de l'environnement [Termes IGN] service écosystémique [Termes IGN] Turquie Résumé : (auteur) Forested landscapes offer high provisioning capacities for many ecosystem services (ES), yet their capabilities may change in time due to multifaceted ES drivers. Therefore, assessing the changes in individual ES is critical for ecosystem-based management. This study analyzes the spatio-temporal changes in ES provided by a forest-dominated protected area in NE, Turkey. To this end, 18 ES were quantified and mapped using the ES matrix approach for 1985 and 2021. Then, the status of the ES and potential drivers of landscape changes were revealed through the assessment of demographic and management structure changes. The results showed that the multiple ES provisioning capacity of the landscape increased by 7% over 35 years. The capacities for “crops” and “livestock” ES decreased for the same period. The most prominent ES were “wild foods,” “erosion regulation,” and “knowledge systems.” Spatially, ES hotspots accumulated in the northern parts and the core zone of the protected area. The most significant changes occurred in the lowlands, mostly composed of degraded forests and coppices as of 1985 after their transformation into productive forests. The spatio-temporal changes in many ES can be attributed to the declaration of the landscape as a protected area in 1994. The removal of anthropogenic pressure and the impact of conservation management can be evaluated as the main drivers for the positive changes in the total ES capacity. Thus, sound policy structures and effective conservation strategies should be further encouraged for increasing protected areas’ capacities to provide the large array of ES. Numéro de notice : A2022-459 Affiliation des auteurs : non IGN Thématique : FORET/GEOMATIQUE Nature : Article DOI : 10.1007/s10661-022-10212-7 Date de publication en ligne : 29/06/2022 En ligne : http://dx.doi.org/10.1007/s10661-022-10212-7 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101260 [article]

Decision tree-based machine learning models for above-ground biomass estimation using multi-source remote sensing data and object-based image analysis / Haifa Tamiminia in Geocarto international, vol 37 n° inconnu ([30/01/2022])  

Public [article]  inGeocarto international > vol 37 n° inconnu [30/01/2022] Titre : Decision tree-based machine learning models for above-ground biomass estimation using multi-source remote sensing data and object-based image analysis Type de document : Article/Communication Auteurs : Haifa Tamiminia, Auteur ; Bahram Salehi, Auteur ; Masoud Mahdianpari, Auteur ; et al., Auteur Année de publication : 2022 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image mixte [Termes IGN] analyse d'image orientée objet [Termes IGN] biomasse aérienne [Termes IGN] boosting adapté [Termes IGN] classification par forêts d'arbres décisionnels [Termes IGN] classification pixellaire [Termes IGN] données d'entrainement (apprentissage automatique) [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] Extreme Gradient Machine [Termes IGN] image ALOS-PALSAR [Termes IGN] image Landsat [Termes IGN] image Sentinel-MSI [Termes IGN] image Sentinel-SAR [Termes IGN] New York (Etats-Unis ; état) [Termes IGN] réserve naturelle Résumé : (auteur) Forest above-ground biomass (AGB) estimation provides valuable information about the carbon cycle. Thus, the overall goal of this paper is to present an approach to enhance the accuracy of the AGB estimation. The main objectives are to: 1) investigate the performance of remote sensing data sources, including airborne light detection and ranging (LiDAR), optical, SAR, and their combination to improve the AGB predictions, 2) examine the capability of tree-based machine learning models, and 3) compare the performance of pixel-based and object-based image analysis (OBIA). To investigate the performance of machine learning models, multiple tree-based algorithms were fitted to predictors derived from airborne LiDAR data, Landsat, Sentinel-2, Sentinel-1, and PALSAR-2/PALSAR SAR data collected within New York’s Adirondack Park. Combining remote sensing data from multiple sources improved the model accuracy (RMSE: 52.14 Mg ha−1 and R2: 0.49). There was no significant difference among gradient boosting machine (GBM), random forest (RF), and extreme gradient boosting (XGBoost) models. In addition, pixel-based and object-based models were compared using the airborne LiDAR-derived AGB raster as a training/testing sample. The OBIA provided the best results with the RMSE of 33.77 Mg ha−1 and R2 of 0.81 for the combination of optical and SAR data in the GBM model. Numéro de notice : A2022-331 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Article DOI : 10.1080/10106049.2022.2071475 Date de publication en ligne : 27/04/2022 En ligne : https://doi.org/10.1080/10106049.2022.2071475 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100607 [article]

Use of remotely sensed data to estimate tree species diversity as an indicator of biodiversity in Blouberg Nature Reserve, South Africa / Mangana Rampheri in Geocarto international, vol 37 n° 2 ([15/01/2022])  

Public [article]  inGeocarto international > vol 37 n° 2 [15/01/2022] . - pp 526 - 542 Titre : Use of remotely sensed data to estimate tree species diversity as an indicator of biodiversity in Blouberg Nature Reserve, South Africa Type de document : Article/Communication Auteurs : Mangana Rampheri, Auteur ; Timothy Dube, Auteur ; Inos Dhau, Auteur Année de publication : 2022 Article en page(s) : pp 526 - 542 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] Afrique du sud (état) [Termes IGN] arbre (flore) [Termes IGN] bande spectrale [Termes IGN] biodiversité végétale [Termes IGN] conservation de la flore [Termes IGN] détection de changement [Termes IGN] espèce végétale [Termes IGN] image Landsat-OLI [Termes IGN] image Landsat-TM [Termes IGN] indice de végétation [Termes IGN] régression [Termes IGN] réserve naturelle Résumé : (auteur) We use remotely sensed data to estimate species diversity in Blouberg Nature Reserve (BNR) in the Limpopo province, South Africa to understand the state of biodiversity since communities’ involvement in conservation initiatives. To achieve this objective, Landsat series data collected before and after community involvement in biodiversity conservation were used in conjunction with selected diversity indices i.e., Shannon-Wiener Index (H’) and Simpson Index (D). Thirty 15 m × 15 m field plots were selected and all trees within each plot were identified, with the help of Botanists. Further, we applied regression analysis to determine the relationship between satellite derived tree species diversity and field observations. The results of the study demonstrated a significant (p  Numéro de notice : A2022-052 Affiliation des auteurs : non IGN Thématique : BIODIVERSITE/FORET/IMAGERIE Nature : Article DOI : 10.1080/10106049.2020.1723717 Date de publication en ligne : 16/04/2020 En ligne : https://doi.org/10.1080/10106049.2020.1723717 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99443 [article]

An assessment of forest loss and its drivers in protected areas on the Copperbelt province of Zambia: 1972–2016 / Darius Phiri in Geomatics, Natural Hazards and Risk, vol 13 n° 1 (2022)  

Public [article]  inGeomatics, Natural Hazards and Risk > vol 13 n° 1 (2022) . - pp 148 - 166 Titre : An assessment of forest loss and its drivers in protected areas on the Copperbelt province of Zambia: 1972–2016 Type de document : Article/Communication Auteurs : Darius Phiri, Auteur ; Collins Chanda, Auteur ; Vincent R. Nyirenda, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 148 - 166 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] aire protégée [Termes IGN] analyse d'image orientée objet [Termes IGN] analyse diachronique [Termes IGN] carte d'occupation du sol [Termes IGN] carte thématique [Termes IGN] classification par arbre de décision [Termes IGN] couvert forestier [Termes IGN] déboisement [Termes IGN] détection de changement [Termes IGN] gestion forestière durable [Termes IGN] protection de la biodiversité [Termes IGN] Zambie Résumé : (auteur) In sub-Saharan Africa, protected areas provide a platform for conserving biodiversity. However, these areas are facing massive pressure due to deforestation, and information on forest dynamics and factors driving the changes in protected areas is generally lacking. This study has two objectives: (1) to assess forest cover changes that have occurred between 1972 and 2016 in Copperbelt Province’s protected areas, and (2) understand the drivers of forest cover changes. The study used thematic land cover maps for six selected years, which were classified using an object-based image analysis (OBIA) approach. We also applied a Classification Tree (CT) approach to assess the drivers of forest cover changes using R statistical software. The findings showed that forest cover in protected areas has been characterised by massive deforestation due to various factors. Between 1972 and 2016, primary and secondary forests showed a decrease of 2,226.43 km2 (11.06%) and an increase of 1,082.93 km2 (4.05%), respectively. The major factors driving forest changes include the levels of precipitation, human population density, elevation, distance from roads, towns and rivers. This study presents consistent information for long-term forest monitoring in protected areas, and informs decision-makers on the levels of deforestation and their drivers for effective forest management. Numéro de notice : A2022-092 Affiliation des auteurs : non IGN Thématique : BIODIVERSITE/FORET/IMAGERIE Nature : Article DOI : 10.1080/19475705.2021.2017021 Date de publication en ligne : 21/12/2021 En ligne : https://doi.org/10.1080/19475705.2021.2017021 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99515 [article]

Classification of mediterranean shrub species from UAV point clouds / Juan Pedro Carbonell-Rivera in Remote sensing, vol 14 n° 1 (January-1 2022)  

Public [article]  inRemote sensing > vol 14 n° 1 (January-1 2022) . - n° 199 Titre : Classification of mediterranean shrub species from UAV point clouds Type de document : Article/Communication Auteurs : Juan Pedro Carbonell-Rivera, Auteur ; Jesus Torralba, Auteur ; Javier Estornell, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 199 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques [Termes IGN] apprentissage automatique [Termes IGN] arbuste [Termes IGN] classification par forêts d'arbres décisionnels [Termes IGN] classification par Perceptron multicouche [Termes IGN] Espagne [Termes IGN] Extreme Gradient Machine [Termes IGN] forêt méditerranéenne [Termes IGN] image captée par drone [Termes IGN] incendie de forêt [Termes IGN] indice de végétation [Termes IGN] modèle de simulation [Termes IGN] modèle numérique de terrain [Termes IGN] parc naturel [Termes IGN] photogrammétrie aérienne [Termes IGN] semis de points Résumé : (auteur) Modelling fire behaviour in forest fires is based on meteorological, topographical, and vegetation data, including species’ type. To accurately parameterise these models, an inventory of the area of analysis with the maximum spatial and temporal resolution is required. This study investigated the use of UAV-based digital aerial photogrammetry (UAV-DAP) point clouds to classify tree and shrub species in Mediterranean forests, and this information is key for the correct generation of wildfire models. In July 2020, two test sites located in the Natural Park of Sierra Calderona (eastern Spain) were analysed, registering 1036 vegetation individuals as reference data, corresponding to 11 shrub and one tree species. Meanwhile, photogrammetric flights were carried out over the test sites, using a UAV DJI Inspire 2 equipped with a Micasense RedEdge multispectral camera. Geometrical, spectral, and neighbour-based features were obtained from the resulting point cloud generated. Using these features, points belonging to tree and shrub species were classified using several machine learning methods, i.e., Decision Trees, Extra Trees, Gradient Boosting, Random Forest, and MultiLayer Perceptron. The best results were obtained using Gradient Boosting, with a mean cross-validation accuracy of 81.7% and 91.5% for test sites 1 and 2, respectively. Once the best classifier was selected, classified points were clustered based on their geometry and tested with evaluation data, and overall accuracies of 81.9% and 96.4% were obtained for test sites 1 and 2, respectively. Results showed that the use of UAV-DAP allows the classification of Mediterranean tree and shrub species. This technique opens a wide range of possibilities, including the identification of species as a first step for further extraction of structure and fuel variables as input for wildfire behaviour models. Numéro de notice : A2022-057 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.3390/rs14010199 En ligne : https://doi.org/10.3390/rs14010199 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99462 [article]

Building fuzzy areal geographical objects from point sets / Jifa Guo in Transactions in GIS, vol 25 n° 6 (December 2021)  

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Quelle efficacité écologique de la politique Natura 2000 sur le domaine terrestre en France ? / Paul Rouveyrol in Sciences, eaux & territoires, article hors-série n° 82 (décembre 2021)  

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Detection of aspen in conifer-dominated boreal forests with seasonal multispectral drone image point clouds / Alwin A. Hardenbol in Silva fennica, vol 55 n° 4 (September 2021)  

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Cloud-native seascape mapping of Mozambique’s Quirimbas National Park with Sentinel-2 / Dimitris Poursanidis in Remote sensing in ecology and conservation, vol 7 n° 2 (June 2021)  

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Rapid ecosystem change at the southern limit of the Canadian Arctic, Torngat Mountains National Park / Emma L. Davis in Remote sensing, vol 13 n° 11 (June-1 2021)  

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Evaluation of light pollution in global protected areas from 1992 to 2018 / Haowei Mu in Remote sensing, vol 13 n° 9 (May-1 2021)  

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Streams and rural abandonment are related to the summer activity of the invasive pest Drosophila suzukii in protected European forests / Alberto Maceda-Veiga in Forest ecology and management, vol 485 ([01/04/2021])  

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Complémentarité des images optiques Sentinel-2 avec les images radar Sentinel-1 et ALOS-PALSAR-2 pour la cartographie de la couverture végétale : application à une aire protégée et ses environs au Nord-Ouest du Maroc via trois algorithmes d’apprentissage automatique / Siham Acharki in Revue Française de Photogrammétrie et de Télédétection, n° 223 (mars - décembre 2021)  

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Shedding light on typical species: implications for habitat monitoring / Gianmaria Bonari in Plant sociology, vol 58 n° 1 ([01/02/2021])  

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Accurate assessment of protected area boundaries for land use planning using 3D GIS / Dilek Tezel in Geocarto international, vol 36 n° 1 ([01/01/2021])  

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