It is important for marine scientists and conservationists to frequently estimate the relative abundance of fish species in their habitats and monitor changes in their populations. As opposed to laborious manual sampling, various automatic computer-based fish sampling solutions in underwater videos have been presented. However, an optimal solution for automatic fish detection and species classification

An Increase in the study of cetaceans' sounds has motivated the development of different automated sound detection and classification techniques. Passive acoustic monitoring (PAM) is extensively used to study these cetaceans' sounds over a period to understand their daily activities within their ecosystem. Using PAM, the gathered sound datasets are usually large and impractical to manually analyse

Insect pests recognition is necessary for crop protection in many areas of the world. In this paper we propose an automatic classifier based on the fusion between saliency methods and convolutional neural networks. Saliency methods are famous image processing algorithms that highlight the most relevant pixels of an image. In this paper, we use three different saliency methods as image preprocessing

Species distribution models that only require presence data provide potentially inaccurate results due to sampling bias and presence data scarcity. Methods have been proposed in the literature to minimize the effects of sampling bias, but without explicitly considering the issue of sample size. A new method developed to better take into account environmental biases in a context of data scarcity is

In this paper, we developed a robust learning method for animal classification from camera-trap images collected in highly cluttered natural scenes and annotated with noisy labels. We proposed two different network structures with and without clean samples to handle noisy labels. We use k-means clustering to divide the training samples into groups with different characteristics, which are then used

Plant phenology is a commonly used indicator representing the impacts on vegetation by the climate and other environmental factors. The use of repeated digital photography provides an opportunity to conduct long-term monitoring of plant phenology and to extract phenological transition dates. Here, we tracked the phenological changes in the flowering tree, Elaeagnus angustifolia L., in the Cherigele

Over the past two decades, bioacoustics and ecoacoustics have attracted a great deal of scholarly attention for their importance in investigating biology and ecology. Consequently, large volumes of academic articles have been published, which make it difficult to clearly understand current research trends. However, global research trends are important for grasping research direction and improving research

Bioclimatic variables are considered as an indispensable data type in species distribution modeling. Such variables are available from the WorldClim database for the entire earth surface and at various spatial resolutions. Moreover, convenient access to real-time satellite data and their products has recently created a new way to produce environmental variables. Therefore, in the present study, it

Plant size diversity or plant size inequality is a much neglected aspect of plant diversity and biodiversity. And yet size diversity plays an important role in understanding the dynamics of plant diversity and natural mechanisms of maintaining plant diversity, which is of great significance in a time of ongoing global changes. In this study, we introduced and investigated a new measure of spatial size

The inherent maintenance capacity of forest ecosystems is an important indicator to evaluate environmental quality. The development of remote sensing time series analysis provides a new perspective and methodology to evaluate such maintenance capacities, that take account of the gradual and subtle changes contained in forest growth over long time periods. This study analyzed forest ecosystem resilience

We present a new, efficient method for extracting three-dimensional animal motion trajectories from thermal stereo video data. Understanding animal behavior in the wild or other unconstrained environments is often based on animal movements. The technology described here is for understanding how bird and bat behavior is affected by the presence of wind turbines, specifically offshore wind turbines which

Bird species that occupy highly specialised ecological niche are susceptible to environmental and climatic change. These species can easily be moved into extinction by small anthropogenic or natural changes to their habitat. It is paramount to understand and assess the uncertainties of the impacts of climate change on the species to adopt adaptation strategies and provide revised management actions

In South Asian countries, Tenualosa ilisha, well known as Hilsa, is considered as one of the most economically important fish species. Production of Hilsa fishes depends on many factors including global warming, water pollution and harvesting. This article proposes a new mathematical model using fuzzy inferences to investigate the impacts of global warming, water pollution and harvesting of juvenile

Deep learning (DL) algorithms are the state of the art in automated classification of wildlife camera trap images. The challenge is that the ecologist cannot know in advance how many images per species they need to collect for model training in order to achieve their desired classification accuracy. In fact there is limited empirical evidence in the context of camera trapping to demonstrate that increasing

In this paper we present ensembles of classifiers for automated animal audio classification, exploiting different data augmentation techniques for training Convolutional Neural Networks (CNNs). The specific animal audio classification problems are i) birds and ii) cat sounds, whose datasets are freely available. We train five different CNNs on the original datasets and on their versions augmented by

Benthic vegetation is arguably one of the most important indicators of the state of marine environment. Assessment of the status of eelgrass (Zostera marina) is commonly done using various remote sensing methods such as aerial photography or satellite images. These methods often fail to capture the true scenario beneath the surface of the water if the water is turbid or the satellite image is masked

Natural calamities like drought cause misery to human lives as well as environment in a variety of ways. The huge adverse consequences and globally predicted climate change accentuate the importance of an effective drought assessment and management system. Lack of universality of available drought indices emphasize the need of an automated system that works globally. Internet of Things (IoT) is highly

The use of camera traps is now widespread and their importance in wildlife studies well understood. Camera trap studies can produce millions of photographs and there is a need for software to help manage photographs efficiently. In this paper, we describe a software system that was built to successfully manage a large behavioral camera trap study that produced more than a million photographs. We describe

Recent advances in ecological modeling have focused on novel methods for characterizing the environment that use presence-only data and machine-learning algorithms to predict the likelihood of species occurrence. These novel methods may have great potential for land suitability applications in the developing world where detailed land cover information is often unavailable or incomplete. This paper