Mapping ecosystem services provided by wetlands at multiple spatiotemporal scales : a case study in Quebec, Canada

Abstract : Wetlands are affected by climate and anthropogenic changes, which influence the ecosystem services (ES) they provide. This study presents a spatially explicit quantification of wetland ESs. The study site is the Yamaska river watershed located in Quebec, Canada. The proposed approach includes four main steps: (1) statistical selection of function indicators (FI) to build a composite ecosystem service indicator (ESI); (2) temporal land use mapping for past (1984), recent (2011) and future scenarios (2050); (3) mapping and quantification of FIs and ESIs at all temporal and spatial scales; and (4) synthesis of multispatial and multitemporal information using a diagram representation. Results present the spatiotemporal evolution of the maintaining habitat ES provided by wetlands in the studied watershed. The historical characterization shows a general degradation of this service on the entire territory for the last 30 years. Multi-scale analyses can target priority sectors in which this service has deteriorated or is lacking. Future scenarios show the urgency to act in order to preserve currently intact areas because even the optimistic scenario indicates that the studied ES would not return to its 1984 state. Finally, the synthesis analysis provides a decision support tool adapted to territory managers. Thus, this study shows that the proposed multi-scale method is reproducible, robust and that it provides simple procedures to assess ES over time and space

Mapping lichen changes in the summer range of the George River Caribou Herd (Québec-Labrador, Canada) using Landsat imagery (1976-1998)

Habitat studies are essential in order to understand the dynamics of migratory caribou herds and to better define management strategies. In this paper, multi-date Landsat images are used to map lichen in the summer range of the George River Caribou Herd (GRCH), Québec-Labrador (Canada), over the period from 1976 to 1998. Multi-Spectral Scanner scenes from the seventies and Thematic Mapper scenes from the eighties and nineties were radiometrically normalized and processed using spectral mixture analysis to produce lichen fraction maps and lichen change maps. Field sites, surveyed during summer campaigns in 2000 and 2001, are used to validate the lichen maps. Results show a good agreement between field data and the lichen results obtained from image analysis. Maps are then interpreted in the context of previous caribou dynamics and habitat studies conducted in the study area over the last three decades. The remote-sensing results confirm the habitat degradation and herd distribution patterns described by other investigators. The period between 1976-1979 and 1985-1986 is characterized by a localized decrease in lichen cover in the southern part of the study area, whereas from 1985-1986 to 1998 the decrease in lichen cover extends northward and westward. This period coincides with the widest extent of the GRCH summer range and activity. The approach presented in this paper provides a valuable means for better understanding the spatio-temporal relation between herd dynamics and distribution, as well as habitat use. Satellite remote sensing imagery is a useful data source, providing timely information over vast and remote territories where caribou populations cannot be surveyed and managed on a frequent basis.&nbsp

Towards the automation of large mammal aerial survey in Africa

editorial reviewedIn African open protected areas, large mammals are often surveyed using manned aircrafts which actively count the animals in sample strips for later density extrapolation to the whole area. Nevertheless, this method may be biased among others by the observer’s detection capability. The use of on-board oblique cameras has recently shown an increase in counting accuracy as a result of indirect photo-interpretation. While this approach appears to reduce some biases, the processing time of the generated data is currently a bottleneck. In recent years, Deep Learning (DL) techniques through dense convolutional neural networks (CNNs) have emerged as a very promising avenue for managing such datasets. However, we are not yet at the stage of full automation of the process (i.e. from acquisition to population estimation). Three challenges were identified: 1) reducing false positives, 2) increasing the precision in close-by individuals, and 3) properly managing the overlap between images to avoid double counting. We focused on the two first aspects and developed a new point-based DL model inspired by crowd counting, that was applied on a challenging oblique aerial dataset containing free ranging livestock herds in heterogeneous open arid landscapes. The model’s performances were then evaluated using localization and counting metrics. The DL model achieved a global F1 score of 0.74 and a RMSE of 9.8 animals per 24 megapixel image, at a processing speed of 3.6 s/image. It showed a valuable ability to detect both isolated animals and those in dense herds. This is auspicious for automation of African mammal surveys but the developed approach still needs to be improved to manage double counting on entire transects. These results emphasize the importance of standardization of data acquisition, with strong spatial and temporal heterogeneities, in order to build robust models that can be used in similar environments and conditions

Counting African Mammal Herds in Aerial Imagery Using Deep Learning: Are Anchor-Based Algorithms the Most Suitable?

editorial reviewedMonitoring wildlife and livestock in protected areas is essential to reach natural ecosystem conservation goals. In large open areas, this is often carried out by direct counting from observers in manned aircrafts flying at low altitude. However, there are several biases associated with this method, resulting in a low accuracy of large groups counts. Unmanned Aerial Vehicles (UAVs) have experienced a significant growth in recent years and seem to be relatively well-suited systems for photographing animals. While UAVs allow for more accurate herd counts than traditional methods, identification and counting are usually indirectly done during a manual time-consuming photo-interpretation process. For several years, machine learning and deep learning techniques have been developed and now show encouraging results for automatic animal detection. Some of them use Convolutional Neural Networks (CNNs) through anchor-based object detectors. These algorithms automatically extract relevant features from images, produce thousands of anchors all over the image and eventually decide which ones actually contain an object. Counting and classification are then achieved by summing and classifying all the selected bounding boxes. While this approach worked well for isolated mammals or sparse herds, it showed limits in close-by individuals by generating too many false positives, resulting in overestimated counts in dense herds. This raises the question: are anchor-based algorithms the most suitable for counting large mammals in aerial imagery? In an attempt to answer this, we built a simple one stage point-based object detector on a dataset acquired over various African landscapes which contains six large mammal species: buffalo (Syncerus caffer), elephant (Loxodonta africana), kob (Kobus kob), topi (Damaliscus lunatus jimela), warthog (Phacochoerus africanus) and waterbuck (Kobus ellipsiprymnus). An adapted version of the CNN DLA-34 was trained on points only (center of the original bounding boxes), splat onto a Focal Inverse Distance Transform (FIDT) map regressed in a pixel-wise manner using the focal loss. During inference, local maxima were extracted from the predicted map to obtain the animals location. Binary model’s performances were then compared to those of the state-of-the-art model, Libra-RCNN. Although our model detected 5% fewer animals compared to the baseline, its precision doubled from 37% to 70%, reducing the number of false positives by one third without using any hard negative mining method. The results obtained also showed a clear increase in precision in close-by individuals areas, letting it appear that a point-based approach seems to be better adapted for animal detection in herds than anchor-based ones. Future work will apply this approach on other animal datasets with different acquisition conditions (e.g. oblique viewing angle, coarser resolution, denser herds) to evaluate its range of use

From crowd to herd counting: How to precisely detect and count African mammals using aerial imagery and deep learning?

peer reviewedRapid growth of human populations in sub-Saharan Africa has led to a simultaneous increase in the number of livestock, often leading to conflicts of use with wildlife in protected areas. To minimize these conflicts, and to meet both communities’ and conservation goals, it is therefore essential to monitor livestock density and their land use. This is usually done by conducting aerial surveys during which aerial images are taken for later counting. Although this approach appears to reduce counting bias, the manual processing of images is timeconsuming. The use of dense convolutional neural networks (CNNs) has emerged as a very promising avenue for processing such datasets. However, typical CNN architectures have detection limits for dense herds and closeby animals. To tackle this problem, this study introduces a new point-based CNN architecture, HerdNet, inspired by crowd counting. It was optimized on challenging oblique aerial images containing herds of camels (Camelus dromedarius), donkeys (Equus asinus), sheep (Ovis aries) and goats (Capra hircus), acquired over heterogeneous arid landscapes of the Ennedi reserve (Chad). This approach was compared to an anchor-based architecture, Faster-RCNN, and a density-based, adapted version of DLA-34 that is typically used in crowd counting. HerdNet achieved a global F1 score of 73.6 % on 24 megapixels images, with a root mean square error of 9.8 animals and at a processing speed of 3.6 s, outperforming the two baselines in terms of localization, counting and speed. It showed better proximity-invariant precision while maintaining equivalent recall to that of Faster-RCNN, thus demonstrating that it is the most suitable approach for detecting and counting large mammals at close range. The only limitation of HerdNet was the slightly weaker identification of species, with an average confusion rate approximately 4 % higher than that of Faster-RCNN. This study provides a new CNN architecture that could be used to develop an automatic livestock counting tool in aerial imagery. The reduced image analysis time could motivate more frequent flights, thus allowing a much finer monitoring of livestock and their land use

Stand-alone Field Trips Based on Geolocation as a Meaningful Source of Learning for Geomatics Students

The project presented is a prototype of self-guided field trips that rely on the use of a mobile device (tablet) and several of its functions. The article discusses the second iteration of a design-led research approach. The design and research issues were mainly centered on the acceptance of the application and its multiple parameters by the teachers, and on the acceptance by the students of its use for learning purposes. An existing application (TaleBlazer) was selected and adapted. Observed acceptance was measured using the UTAUT model combined with qualitative feedback.Le projet présenté est un prototype de sorties terrain autoguidées exploitant un appareil mobile (tablette) et plusieurs de ses fonctions. L’article traite de la deuxième itération d’une démarche de recherche orientée par la conception. Les enjeux de la conception et de la recherche étaient principalement centrés sur l’acceptation de l’application et de ses multiples paramètres par les enseignants et sur l’acceptation par les étudiants de son utilisation à des fins d’apprentissage. Une application existante (TaleBlazer) a été sélectionnée et adaptée. L’acceptation observée a été mesurée à l’aide du modèle UTAUT combiné à des commentaires qualitatifs

Modélisation de réseaux écologiques et impacts des choix méthodologiques sur leur configuration spatiale : analyse de cas en Estrie (Québec, Canada)

Ecological networks are designed to maintain biodiversity and ecological processes by protecting habitats and their connectivity. Several approaches can be used to define the components of these networks at each stage of their design. These choices are a major source of uncertainty that influence the spatial configuration of the networks obtained, but they have not been extensively studied. In this study, several methods were applied to the steps for selecting core areas and designing corridors for the same territory in the Eastern Townships, Quebec. The aim of the study was to design an ecological network suitable for an indicator species, the Pileated Woodpecker (Dryocopus pileatus). Two methods for selecting core areas were tested : a multi-criteria analysis and a habitat suitability index. These two methods, which were also used in the step for creating matrices of resistance to movement, were combined with three methods to design corridors : least-cost path, least-cost corridor, and circuit theory. Six ecological networks were created and compared. The results show considerable differences in the spatial configuration of the networks, whether in terms of the area and perimeter of different elements or the corridor width. These results are discussed in relation to two limiting factors for these methodological steps, which are data availability and the representativeness of the models. We also provide suggestions to help decision makers facing the many possible scenarios for ecological networks

Modélisation de réseaux écologiques et impacts des choix méthodologiques sur leur configuration spatiale : analyse de cas en Estrie (Québec, Canada)

Les réseaux écologiques visent à maintenir la biodiversité et les processus écologiques en protégeant les habitats et leur connectivité. Plusieurs approches peuvent être utilisées pour délimiter les composantes de ces réseaux, et ce, à chaque étape de la conception. Ces différentes possibilités constituent une source majeure d’incertitude qui influence la configuration spatiale des réseaux obtenus, mais qui est peu étudiée et documentée. Dans cette étude, plusieurs méthodes ont été appliquées aux étapes de sélection des zones nodales et de tracé des corridors sur un même territoire en Estrie, Québec. L’étude visait à concevoir un réseau écologique adapté à une espèce indicatrice, le Grand Pic (Dryocopus pileatus). Deux méthodes de sélection des zones nodales ont été testées : l’analyse multicritères et l’indice de qualité d’habitat. Ces deux méthodes, reprises également à l’étape de création des matrices de résistance aux déplacements, ont été combinées à trois méthodes de tracé des corridors : le chemin de moindre coût, le corridor de moindre coût et la théorie des circuits. Six réseaux écologiques résultants ont été créés et comparés. Les résultats montrent d’importantes différences de configuration spatiale entre les réseaux que ce soit en termes de superficie et de périmètre des différents éléments que de largeurs de corridors. Ces résultats sont discutés en lien avec deux facteurs limitants concernant ces étapes méthodologiques soient la disponibilité des données et la représentativité des modèles. Des pistes de réflexion sont également proposées afin d’outiller davantage les décideurs dans leurs choix face aux nombreux scénarios possibles de réseaux écologiques.Ecological networks are designed to maintain biodiversity and ecological processes by protecting habitats and their connectivity. Several approaches can be used to define the components of these networks at each stage of their design. These choices are a major source of uncertainty that influence the spatial configuration of the networks obtained, but they have not been extensively studied. In this study, several methods were applied to the steps for selecting core areas and designing corridors for the same territory in the Eastern Townships, Quebec. The aim of the study was to design an ecological network suitable for an indicator species, the Pileated Woodpecker (Dryocopus pileatus). Two methods for selecting core areas were tested : a multi-criteria analysis and a habitat suitability index. These two methods, which were also used in the step for creating matrices of resistance to movement, were combined with three methods to design corridors : least-cost path, least-cost corridor, and circuit theory. Six ecological networks were created and compared. The results show considerable differences in the spatial configuration of the networks, whether in terms of the area and perimeter of different elements or the corridor width. These results are discussed in relation to two limiting factors for these methodological steps, which are data availability and the representativeness of the models. We also provide suggestions to help decision makers facing the many possible scenarios for ecological networks

Modélisation de réseaux écologiques et impacts des choix méthodologiques sur leur configuration spatiale : analyse de cas en Estrie (Québec, Canada)

Les réseaux écologiques visent à maintenir la biodiversité et les processus écologiques en protégeant les habitats et leur connectivité. Plusieurs approches peuvent être utilisées pour délimiter les composantes de ces réseaux, et ce, à chaque étape de la conception. Ces différentes possibilités constituent une source majeure d’incertitude qui influence la configuration spatiale des réseaux obtenus, mais qui est peu étudiée et documentée. Dans cette étude, plusieurs méthodes ont été appliquées aux étapes de sélection des zones nodales et de tracé des corridors sur un même territoire en Estrie, Québec. L’étude visait à concevoir un réseau écologique adapté à une espèce indicatrice, le Grand Pic (Dryocopus pileatus). Deux méthodes de sélection des zones nodales ont été testées : l’analyse multicritères et l’indice de qualité d’habitat. Ces deux méthodes, reprises également à l’étape de création des matrices de résistance aux déplacements, ont été combinées à trois méthodes de tracé des corridors : le chemin de moindre coût, le corridor de moindre coût et la théorie des circuits. Six réseaux écologiques résultants ont été créés et comparés. Les résultats montrent d’importantes différences de configuration spatiale entre les réseaux que ce soit en termes de superficie et de périmètre des différents éléments que de largeurs de corridors. Ces résultats sont discutés en lien avec deux facteurs limitants concernant ces étapes méthodologiques soient la disponibilité des données et la représentativité des modèles. Des pistes de réflexion sont également proposées afin d’outiller davantage les décideurs dans leurs choix face aux nombreux scénarios possibles de réseaux écologiques.Ecological networks are designed to maintain biodiversity and ecological processes by protecting habitats and their connectivity. Several approaches can be used to define the components of these networks at each stage of their design. These choices are a major source of uncertainty that influence the spatial configuration of the networks obtained, but they have not been extensively studied. In this study, several methods were applied to the steps for selecting core areas and designing corridors for the same territory in the Eastern Townships, Quebec. The aim of the study was to design an ecological network suitable for an indicator species, the Pileated Woodpecker (Dryocopus pileatus). Two methods for selecting core areas were tested : a multi-criteria analysis and a habitat suitability index. These two methods, which were also used in the step for creating matrices of resistance to movement, were combined with three methods to design corridors : least-cost path, least-cost corridor, and circuit theory. Six ecological networks were created and compared. The results show considerable differences in the spatial configuration of the networks, whether in terms of the area and perimeter of different elements or the corridor width. These results are discussed in relation to two limiting factors for these methodological steps, which are data availability and the representativeness of the models. We also provide suggestions to help decision makers facing the many possible scenarios for ecological networks