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

Using drone technology to map village lands in protected areas of the democratic republic of Congo

peer reviewedLes aires protégées de la République démocratique du Congo (RDC) sont menacées par diverses pressions anthropiques nécessitant un suivi fréquent et précis. Le mini-drone Falcon équipé d’un appareil photo numérique Sony NEX-7 a été utilisé pour cartographier et suivre la dynamique d’un terroir villageois dans le Domaine de chasse de Mondo Missa à l’est du Parc national de la Garamba, au nord-est de la RDC. Un total de 3 143 photos acquises en avril et juillet 2015, avec une résolution au sol de 8 cm/pixel, a été orthorectifié. La cartographie a porté sur une zone de 114 ha. Les ortho-images ont d’abord été segmentées, les segments étant ensuite classés manuellement par photo-interprétation. Des changements notables ont été constatés entre les deux dates. Les zones des forêts et savanes ont perdu 6,5 ha (86,6 à 80,1 ha). Les jachères sont passées de 16,9 à 8,2 ha, les défriches de 4,1 à 10,0 ha. Les cultures saisonnières ont connu une variation allant de 3,2 à 11,8 ha. La taille moyenne des parcelles cultivées est de 0,2 ha (s = 0,14 ha ; n = 50). Enfin, la surface occupée par les arbres isolés a peu évolué (de 1,3 à 1,9 ha), celle des implantations humaines étant constante (1,7 ha). Ces résultats traduisent le fait que l’expansion de l’agriculture itinérante sur brûlis induit une conversion des habitats naturels et une modification de la composition végétale. Les aéronefs sans pilote à bord permettent de réaliser une cartographie précise et une surveillance rapide des changements d’affectation des terres à petite échelle dans les aires protégées des forêts et savanes tropicales. Ils offrent donc une solution efficace pour évaluer la déforestation et la dégradation au sein des espaces occupés par les communautés locales. Cette évaluation représente un enjeu important dans le processus REDD+ qui envisage de quantifier avec précision ces évolutions

Satellite Earth observation data to identify anthropogenic pressures in selected protected areas

Protected areas are experiencing increased levels of human pressure. To enable appropriate conservation action, it is critical to map and monitor changes in the type and extent of land cover/use and habitat classes, which can be related to human pressures over time. Satellite Earth observation (EO) data and techniques offer the opportunity to detect such changes. Yet association with field information and expert interpretation by ecologists is required to interpret, qualify and link these changes to human pressure. There is thus an urgent need to harmonize the technical background of experts in the field of EO data analysis with the terminology of ecologists, protected area management authorities and policy makers in order to provide meaningful, context-specific value-added EO products. This paper builds on the DPSIR framework, providing a terminology to relate the concepts of state, pressures, and drivers with the application of EO analysis. The type of pressure can be inferred through the detection of changes in state (i.e. changes in land cover and/or habitat type and/or condition). Four broad categories of changes in state are identified, i.e. land cover/habitat conversion, land cover/habitat modification, habitat fragmentation and changes in landscape connectivity, and changes in plant community structure. These categories of change in state can be mapped through EO analyses, with the goal of using expert judgement to relate changes in state to causal direct anthropogenic pressures. Drawing on expert knowledge, a set of protected areas located in diverse socio-ecological contexts and subject to a variety of pressures are analysed to (a) link the four categories of changes in state of land cover/habitats to the drivers (anthropogenic pressure), as relevant to specific target land cover and habitat classes; (b) identify (for pressure mapping) the most appropriate spatial and temporal EO data sources as well as interpretations from ecologists and field data useful in connection with EO data analysis. We provide detailed examples for two protected areas, demonstrating the use of EO data for detection of land cover/habitat change, coupled with expert interpretation to relate such change to specific anthropogenic pressures. We conclude with a discussion of the limitations and feasibility of using EO data and techniques to identify anthropogenic pressures, suggesting additional research efforts required in this direction

Satellite Earth observation data to identify anthropogenic pressures in selected protected areas

Protected areas are experiencing increased levels of human pressure. To enable appropriate conserva-tion action, it is critical to map and monitor changes in the type and extent of land cover/use and habitatclasses, which can be related to human pressures over time. Satellite Earth observation (EO) data andtechniques offer the opportunity to detect such changes. Yet association with field information and expertinterpretation by ecologists is required to interpret, qualify and link these changes to human pressure.There is thus an urgent need to harmonize the technical background of experts in the field of EO dataanalysis with the terminology of ecologists, protected area management authorities and policy makers inorder to provide meaningful, context-specific value-added EO products. This paper builds on the DPSIRframework, providing a terminology to relate the concepts of state, pressures, and drivers with the appli-cation of EO analysis. The type of pressure can be inferred through the detection of changes in state (i.e.changes in land cover and/or habitat type and/or condition). Four broad categories of changes in stateare identified, i.e. land cover/habitat conversion, land cover/habitat modification, habitat fragmentationand changes in landscape connectivity, and changes in plant community structure. These categories ofchange in state can be mapped through EO analyses, with the goal of using expert judgement to relatechanges in state to causal direct anthropogenic pressures. Drawing on expert knowledge, a set of pro-tected areas located in diverse socio-ecological contexts and subject to a variety of pressures are analysedto (a) link the four categories of changes in state of land cover/habitats to the drivers (anthropogenic pres-sure), as relevant to specific target land cover and habitat classes; (b) identify (for pressure mapping) themost appropriate spatial and temporal EO data sources as well as interpretations from ecologists andfield data useful in connection with EO data analysis. We provide detailed examples for two protectedareas, demonstrating the use of EO data for detection of land cover/habitat change, coupled with expertinterpretation to relate such change to specific anthropogenic pressures. We conclude with a discussionof the limitations and feasibility of using EO data and techniques to identify anthropogenic pressures,suggesting additional research efforts required in this direction

Monitoring and recording changes in natural landscapes: A case study from two coastal wetlands in se italy

This study analyzed and evaluated the changes that occurred in two coastal wetlands, characterized by complex and fragmented landscape patterns, in Southern Italy, which were moni-tored over a period of seven years from 2007 to 2014. Furthermore, the performances of two Land Cover (LC) and habitat taxonomies, compared for their suitability in mapping the identified changes, were assessed. A post-mapping method was adopted to detect the habitat/LC changes that occurred in the study period. Various changes were identified, both inter-class changes (class conversions) and intra-class changes (class modifications), and quantified by means of transition matrices. Conversions were easily mapped, while the modification mapping depended on the taxonomy adopted: the Land Cover Classification System (LCCS) allowed the detection of morpho-structural changes in woody vegetation, but the European Nature Information System (EUNIS) showed a higher thematic resolution for the salt marsh types. The detected changes were related to specific impacts, pressures and underlying factors. Landscape indices highlighted different trends in landscape richness and complexity in the two sites. Changes are occurring very quickly in the observed coastal sites and the ongoing dynamics are strictly related to their small size and complexity. For effective monitoring and detection of change in these environments, the coupling of EUNIS and LCCS is suggested