Deep learning habitat modeling for moving organisms in rapidly changing estuarine environments: A case of two fishes

Guénard, Guillaume; Morin, Jean; Matte, Pascal; Secretan, Yves; Valiquette, Eliane; Mingelbier, Marc

doi:10.1016/j.ecss.2020.106713

Cited by 13 publications

(6 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The utilization of AI in the preservation of aquatic alongside marine biodiversity as well as water resources has garnered considerable research interest in the past decade. Artificial Intelligence (AI) and Machine Learning (ML) models have been employed to forecast stream flow [75] , assess water quality [76][77][78][79][80][81] , detect water pollution alongside toxicology [82,83] , anticipate changes in aquatic and marine biodiversity [84][85][86] , predict species distribution and map habitats [87,88] , as well as recognize and classify marine and aquatic species [89][90][91][92][93][94][95][96][97] . The aforementioned AI research in aquatic alongside marine biodiversity as well as water resource conservation emphasizes the crucial role of AI in developing innovative technology to discover previously unknown aspects of conservation and potential risks to the structures and functions of aquatic and marine ecosystems.…”

Section: Water Resource Conservation and Marine Biodiversitymentioning

confidence: 99%

Artificial intelligence and machine learning applications in forest management and biodiversity conservation

Raihan

2023

Nat. Resour. Conserv. Res.

View full text Add to dashboard Cite

The recent progress in data science, along with the transformation in digital and satellite technology, has enhanced the capacity for artificial intelligence (AI) applications in the forestry and wildlife domains. Nevertheless, the swift proliferation of developmental projects, agricultural, and urban areas pose a significant threat to biodiversity on a global scale. Hence, the integration of emerging technologies such as AI in the fields of forests and biodiversity might facilitate the efficient surveillance, administration, and preservation of biodiversity and forest resources. The objective of this paper is to present a comprehensive review of how AI and machine learning (ML) algorithms are utilized in the forestry sector and biodiversity conservation worldwide. Furthermore, this research examines the difficulties encountered while implementing AI technology in the fields of forestry and biodiversity. Enhancing the availability of extensive data pertaining to forests and biodiversity, along with the utilization of cloud computing and digital and satellite technology, can facilitate the wider acceptance and implementation of AI technology. The findings of this study would inspire forest officials, scientists, researchers, and conservationists to investigate the potential of AI technology for the purposes of forest management and biodiversity conservation.

show abstract

Section: Water Resource Conservation and Marine Biodiversitymentioning

confidence: 99%

Artificial intelligence and machine learning applications in forest management and biodiversity conservation

Raihan

2023

Nat. Resour. Conserv. Res.

View full text Add to dashboard Cite

show abstract

“…Such an approach demonstrates a potential solution to the ‘snapshot’ nature of surveys with high spatial scope, which typically occur infrequently. Hydrodynamic modelling combined with biotelemetry also offers an effective solution to characterize the temporal dynamics of habitat suitability under rapidly changing environmental conditions, such as in estuaries (Guénard et al ., 2020). Furthermore, machine learning algorithms are emerging as a critical tool to extract patterns in very large data sets integrating spatially continuous river data over time (Carbonneau et al ., 2020).…”

Section: Future Directions and New Frontiersmentioning

confidence: 99%

Riverscape approaches in practice: perspectives and applications

et al. 2021

View full text Add to dashboard Cite

Landscape perspectives in riverine ecology have been undertaken increasingly in the last 30 years, leading aquatic ecologists to develop a diverse set of approaches for conceptualizing, mapping and understanding ‘riverscapes’. Spatiotemporally explicit perspectives of rivers and their biota nested within the socio‐ecological landscape now provide guiding principles and approaches in inland fisheries and watershed management. During the last two decades, scientific literature on riverscapes has increased rapidly, indicating that the term and associated approaches are serving an important purpose in freshwater science and management. We trace the origins and theoretical foundations of riverscape perspectives and approaches and examine trends in the published literature to assess the state of the science and demonstrate how they are being applied to address recent challenges in the management of riverine ecosystems. We focus on approaches for studying and visualizing rivers and streams with remote sensing, modelling and sampling designs that enable pattern detection as seen from above (e.g. river channel, floodplain, and riparian areas) but also into the water itself (e.g. aquatic organisms and the aqueous environment). Key concepts from landscape ecology that are central to riverscape approaches are heterogeneity, scale (resolution, extent and scope) and connectivity (structural and functional), which underpin spatial and temporal aspects of study design, data collection and analysis. Mapping of physical and biological characteristics of rivers and floodplains with high‐resolution, spatially intensive techniques improves understanding of the causes and ecological consequences of spatial patterns at multiple scales. This information is crucial for managing river ecosystems, especially for the successful implementation of conservation, restoration and monitoring programs. Recent advances in remote sensing, field‐sampling approaches and geospatial technology are making it increasingly feasible to collect high‐resolution data over larger scales in space and time. We highlight challenges and opportunities and discuss future avenues of research with emerging tools that can potentially help to overcome obstacles to collecting, analysing and displaying these data. This synthesis is intended to help researchers and resource managers understand and apply these concepts and approaches to address real‐world problems in freshwater management.

show abstract

“…As shown in Table 6, TP denotes the sample that is positive in actuality and positive in prediction; FP denotes the sample that is negative in actuality but positive in prediction; FN denotes the sample that is positive in actuality but negative in prediction; TN denotes the sample that is negative in actuality and negative in prediction. With the confusion matrix, precision and recall are defined in Equations (11) and (12):…”

Section: The Evaluation Indicatorsmentioning

confidence: 99%

MRFF-YOLO: A Multi-Receptive Fields Fusion Network for Remote Sensing Target Detection

Xu¹,

Wu²

2020

Remote Sensing

View full text Add to dashboard Cite

High-altitude remote sensing target detection has problems related to its low precision and low detection rate. In order to enhance the performance of detecting remote sensing targets, a new YOLO (You Only Look Once)-V3-based algorithm was proposed. In our improved YOLO-V3, we introduced the concept of multi-receptive fields to enhance the performance of feature extraction. Therefore, the proposed model was termed Multi-Receptive Fields Fusion YOLO (MRFF-YOLO). In addition, to address the flaws of YOLO-V3 in detecting small targets, we increased the detection layers from three to four. Moreover, in order to avoid gradient fading, the structure of improved DenseNet was chosen in the detection layers. We compared our approach (MRFF-YOLO) with YOLO-V3 and other state-of-the-art target detection algorithms on an Remote Sensing Object Detection (RSOD) dataset and a dataset of Object Detection in Aerial Images (UCS-AOD). With a series of improvements, the mAP (mean average precision) of MRFF-YOLO increased from 77.10% to 88.33% in the RSOD dataset and increased from 75.67% to 90.76% in the UCS-AOD dataset. The leaking detection rates are also greatly reduced, especially for small targets. The experimental results showed that our approach achieved better performance than traditional YOLO-V3 and other state-of-the-art models for remote sensing target detection.

show abstract

Deep learning habitat modeling for moving organisms in rapidly changing estuarine environments: A case of two fishes

Cited by 13 publications

References 23 publications

Artificial intelligence and machine learning applications in forest management and biodiversity conservation

Artificial intelligence and machine learning applications in forest management and biodiversity conservation

Riverscape approaches in practice: perspectives and applications

MRFF-YOLO: A Multi-Receptive Fields Fusion Network for Remote Sensing Target Detection

Contact Info

Product

Resources

About