A fast and simple method to assess land use statistics using very high resolution imagery from mini-drone

peer reviewedLe suivi de l’utilisation des terres par télédétection a récemment connu un essor important. Cela s’explique par une accessibilité accrue et souvent gratuite des images à (très) haute résolution ainsi que par le développement d’applications web destinées au suivi de l’utilisation des terres. L’accès à ces applications reste cependant soumis à l’existence d’une connexion Internet fiable faisant encore défaut dans certaines régions du globe. Dans ce contexte, la présente étude décrit une méthode permettant de produire des statistiques sur l’évolution de l’occupation du sol en réalisant une photo-interprétation par point sur des images en couleurs vraies à très haute résolution produites par mini-drone. La méthode utilise une application (PINT pour Photo-INTerprétation) intégrée dans le logiciel open source QGIS. Les surfaces de différentes occupations du sol sont dérivées des estimations des proportions de points affectées à chaque classe à partir d’une grille systématique. Pour illustrer l’intérêt de l’outil, l’étude considère les statistiques d’occupation du sol au sein de deux terroirs villageois du Complexe d’aires protégées de la Garamba, en République démocratique du Congo. Les résultats obtenus sont comparés avec ceux d’une cartographie de référence basée sur une photo-interprétation exhaustive après segmentation des images. Les écarts entre surfaces estimées par échantillonnage et surfaces de référence varient entre 0,2 % et 6,1 % pour les principales occupations du sol (forêts et savanes, défriches, jachères, implantations humaines et cultures). Des différences plus importantes (17,4 % et 13,4 %) sont enregistrées pour la classe « arbres isolés ». Le temps global de mise en œuvre de la méthode est de l’ordre de 60 ha par heure d’opérateur. L’utilisation du plugin PINT avec des images « drone » constitue une solution pertinente pour estimer des statistiques d’occupation du sol dans des régions web-isolées et pour des sites d’étendues de quelques (dizaines de) km².Land use monitoring by remote sensing techniques has been developing rapidly, thanks to much easier access, often free of charge, to (very) high-resolution images, and to the development of specific Web applications for land use monitoring.However, access to these applications depends on the existence of a reliable internet connection, which is still lacking in some regions of the world. This study describes a land-use monitoring method based on point-by-point photo-interpretation of very high-resolution images acquired by small drones. The method requires the integration of an application (PINT, for Photo-INTerpretation) into QGIS Open source software. The areas occupied by different land uses are derived from the estimated proportions of the points allocated to each land-use class, based on a systematic grid. To illustrate the advantages of the tool, this study investigated the land-use statistics for two villages in the Greater Garamba Complex of protected areas, in the Democratic Republic of Congo. The results obtained were compared with those of a reference map, on the basis of exhaustive photo-interpretation after segmentation of the images. The differences between the areas estimated by sampling and the reference areas vary from 0.2% to 6.1% for the main land uses (forests and savannas, clearings, fallows, human settlements and crops). Larger differences (17.4% and 13.4%) were recorded for the “isolated trees” class. Implementing the method takes about 1 hour per operator for 60 ha. Using the PINT plugin with drone images appears to be a relevant solution to estimate land-use statistics in Web-isolated regions, for areas of a few to a few dozen km²

Harmonised projections of future forest resources in Europe

Data PaperAbstract • Key message A dataset of forest resource projections in 23 European countries to 2040 has been prepared for fores trelated policy analysis and decision-making. Due to applying harmonised definitions, while maintaining country-specific forestry practices, the projections should be usable from national to international levels. The dataset can be accessed at https://doi.org/10.5061/dryad.4t880qh. The associated metadata are available at https://metadata-afs.nancy.inra.fr/ geonetwork/srv/eng/catalog.search#/metadata/8f93e0d6-b524-43bd-bdb8-621ad5ae6fa9info:eu-repo/semantics/publishedVersio

Forest inventory of Wallonia - 1994-2012 results

Depuis 1994, l’Inventaire Permanent des Ressources forestières de Wallonie (IPRFW) assure le monitoring de l’ensemble des forêts du territoire. Source importante d’informations objectives pour une gestion forestière raisonnée et durable, l’IPRFW constitue un outil précieux pour la politique forestière régionale, la recherche, l’industrie du bois et aussi pour toutes celles et ceux qui désirent simplement en savoir plus sur la forêt wallonne. Dans cet ouvrage, ils trouveront tout d’abord un état des lieux de nos forêts wallonnes établi grâce aux données de l’ensemble du 1er cycle de mesures (1994 à 2008). Leur évolution récente est ensuite décrite en exploitant les données du 2ème cycle de mesures (2008 – 2012), qui est toujours en cours.Since 1994, the Regional Forest Inventory of Wallonia (RFIW) is in charge of the monitoring of all the forests of Wallonia. Main source of objective data for a reasoned and sustainable forest management, the RFIW is a precious tool for regional forest policy, research, wood industries and also all people who simply want to know more about forests in Wallonia. This book contains results from the first inventory (1994-2008) and from the beginning of the second inventory (2008-2012)

SIMREG, a tree-level distance-independent model to simulate forest dynamics and management from national forest inventory (NFI) data

SIMREG is a non-deterministic tree-level distance independent forest model that can simulate forest growth, yield and management on a regional scale while representing the wide diversity of composition, structure and management found in forest stands. It is composed of several sub-models to represent the main forest dynamics (growth, recruitment, removal, clearcut and reforestation) and to account for species composition, stand density, tree size and social status, forest ownership type and some sites characteristics. We used the data collected by the permanent forest inventory of Wallonia (IPRFW) between 1994 and 2015 to calibrate SIMREG and forecast the development of Wallonia's 479 500 ha of productive forest (465 million simulated trees) until 2050. According to our simulation, the harvesting rate of Norway spruce (the main production species) is currently unsustainable and it is gradually being replaced by other species such as Douglas-fir, larch and various hardwoods. It appears that in terms of total softwood volume production, the higher production level of Douglas-fir and larch should eventually compensate for the decline in spruce. In contrast, the harvest rate in hardwood stands is around 75% of the annual yield, resulting in a steady increase in the total hardwood stock of about 600 000 m³ per year. Our methodology is easily replicable and the data required for sub-model calibration are consistent with those measured by most permanent NFIs, so our forest simulation model could be adapted to other regions and countries.Plan quinquennal de recherches forestière 2019-202

Correction to: Harmonised projections of future forest resources in Europe (Annals of Forest Science, (2019), 76, 3, (79), 10.1007/s13595-019-0863-6)

The original article was erroneously published without applying all the provided proof corrections in Section 5 and Table 1. © 2019, INRA and Springer-Verlag France SAS, part of Springer Nature

Harmonised projections of future forest resources in Europe

• Key message A dataset of forest resource projections in 23 European countries to 2040 has been prepared for forest-related policy analysis and decision-making. Due to applying harmonised definitions, while maintaining country-specific forestry practices, the projections should be usable from national to international levels. The dataset can be accessed at https://doi.org/10.5061/dryad.4t880qh . The associated metadata are available at https://metadata-afs.nancy.inra.fr/geonetwork/srv/eng/catalog.search#/metadata/8f93e0d6-b524-43bd-bdb8-621ad5ae6fa9