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In accordance with the 92/43/EEC "Habitats" Directive, Mediterranean temporary ponds are identified as a priority natural habitat within the European context. They are a very interesting and unique habitat type, as ecological conditions can vary greatly in a short period of time. Due to their small size, many Mediterranean hydrophytic bryophytes typical of this habitat are often overlooked or misinterpreted. Their distribution, habitats, ecology, and strategies are generally poorly understood. Several of them are currently considered rare or endangered in the Mediterranean. As these ponds are particularly sensitive to human activities and natural changes, such bryophytes and associated vegetation communities may be at risk. This study is focused on their floristic variability in different environmental conditions in two sites of particular phytogeographic interest in the Mediterranean area. In the Sardinian Pauli of Giara, 56 taxa (50 Bryophyta and 6 Marchantiophyta) were found, and in the Umbria Piana di Ferretto, 54 taxa (34 Bryophyta and 20 Marchantiophyta) were documented. The taxa from the two areas were analysed and compared. Life strategies, life macroforms, light and moisture preferences, chorological elements, and moisture belts were considered. The data are presented here together with information on the phytogeography and ecology of the species recorded. The findings indicate that a bespoke monitoring strategy and dedicated conservation measures are essential for the effective protection of bryophytes, ensuring the achievement of meaningful and sustainable conservation outcomes. [ABSTRACT FROM AUTHOR]

Agroforestry systems (AFS) are important for biodiversity conservation outside protected areas. The presence of shade trees in AFS form structurally complex habitats that provide food for many species. Habitat complexity is considered an essential biodiversity variable and its characterization is now possible using remote sensing techniques, including 3D point clouds derived from images obtained with unmanned aerial vehicles (UAVs). However, studies evaluating the relationship between canopy structure and variables derived from 3D clouds are rare for AFS, especially for the tropical Andes. Here, we show how six important variables of canopy structure can be predicted across a canopy structure gradient from AFS with cacao and coffee to a natural forest using characteristics extracted from the 3D point clouds and multiple linear regression. For leaf area index the best model obtained an R2 of 0.82 with a relative RMSE = 24%, for canopy cover an R2 of 0.81 and relative RMSE = 13%, for above-ground biomass an R2 of 0.81 and relative RMSE = 10%, the density of shade trees was predicted with an R2 of 0.66 and relative RMSE = 34%, the mean height and the standard deviation of height in the canopy obtained an R2 of 0.82 and 0.79 respectively, and relative RMSE of 18% for both. The approach presented in this study allows an accurate characterization of the canopy structure of AFS using UAVs, which can be useful for assessing above-ground biomass and biodiversity in tropical agricultural landscapes to monitor sustainable management practices and derive payments for ecosystem services. [ABSTRACT FROM AUTHOR]

Accurate long-term monitoring of wetlands using satellite archives is crucial for effective conservation. While new methods based on temporal profile classification have been useful for long-term monitoring of wetlands, their advantages over traditional classification methods have not yet been demonstrated. This study aimed to compare continuous change detection (using the continuous change detection and classification (CCDC) algorithm) to traditional post-classification change detection for monitoring wetland changes between 1984 and 2022 in a temperate coastal marsh (Marais Poitevin, France) from the Landsat archive. The reference dataset was collected mainly from field observations and used to train and test a random forest classifier. The accuracy of the resulting change map was then assessed for both methods using validation points collected via visual interpretation of historical aerial photographs and Landsat temporal profiles. The change map derived from CCDC had much higher unbiased overall accuracy (0.86 ± 0.02) than that derived from post-classification change detection (0.51 ± 0.03). In addition, wetland loss was much higher than wetland gain (18 % and 2 % of the area, respectively) and was due mainly to conversion of grassland to cropland and urbanization. The study demonstrated that, unlike traditional post-classification change detection, continuous change detection provides maps of wetland changes sufficiently accurate for operational use by managers. The study also confirmed the ongoing impact of agricultural intensification and artificialization on wetland degradation in Europe.

Invasive alien plant species (IPS) are one of the major threats to biodiversity and ecosystem services. As the dynamics of biological invasions by non-native plant species are expected to intensify with climate change, there is an increasing need to provide accessible information on the distribution of IPS to improve environmental management programmes. Monitoring the probability of occurrence of IPS is therefore essential to limit their spread, as control measures are most effective in the early stages of invasion. This article presents IPS Monitor, a tool developed to monitor habitat suitability for IPS in Germany under current and projected climate conditions. Developed from previous research on IPS impacts and habitat modelling, the tool facilitates the visualisation of habitat suitability for 45 IPS through digital web maps and fact sheets. IPS Monitor acts as a bridge between scientific research and its application, aiming to support decision-making by conservationists, policy-makers and other stakeholders. It provides a scientific basis for developing targeted strategies against the spread of IPS and enables integrated management approaches by providing access to synthesised research and predictive modelling.

Seasonally astatic aquatic habitats are important ecologically, municipally, and agriculturally. Regulatory agencies and conservation organizations have developed various plans for protecting or constructing temporary wetlands, resulting in habitat monitoring requirements, particularly as relates to restoration and constructed habitats. Unfortunately, there has been no effort to develop a unified, consistent method for wetland biological monitoring. This is particularly true for habitats important in a regulatory sense. We conducted macroinvertebrate bioassessment in constructed vernal pools in California, USA, to assess habitat functionality. This tool is modified from aquatic bioassessment; a primary tool of regulatory agencies in measuring habitat health and water quality and should be equally applicable to seasonally astatic wetlands globally. [ABSTRACT FROM AUTHOR]

Seasonally astatic aquatic habitats are important ecologically, municipally, and agriculturally. Regulatory agencies and conservation organizations have developed various plans for protecting or constructing temporary wetlands, resulting in habitat monitoring requirements, particularly as relates to restoration and constructed habitats. Unfortunately, there has been no effort to develop a unified, consistent method for wetland biological monitoring. In Part I, we presented a quantifiable, replicable method for assessing seasonally astatic wetlands, which would allow for direct comparison between individual wetlands, wetland sites, and wetland types. Here in Part II, we apply the method and present the results from more than a decade of a data on two disparate sites that support California vernal pool habitats. These habitats include natural, restored, and constructed vernal pools. Our results demonstrate that the method we present yields reliable, statistically useful, and actionable data and provides a better method for assessing astatic wetland ecological health and the persistence of federally listed vernal pool crustaceans than other methods so far employed. [ABSTRACT FROM AUTHOR]

Three species of catfish, Silurus biwaensis, Silurus lithophilus, and Silurus asotus, are identified in Lake Biwa. Notably, S. biwaensis and S. lithophilus are endemic species in Lake Biwa; however, their detailed distribution and ecology almost remain unknown. In this study, we developed catfish species-specific primer probe sets based on their whole genome. Using these, we analyzed the water samples collected from three tanks where S. biwaensis, S. lithophilus, and S. asotus were experimentally reared, respectively, and established a new detection system after confirming each primer probe set to be effective. Finally, we conducted environmental DNA analysis for catfishes in Lake Biwa using field samples. Overall, our results showed that environmental DNA analysis is a useful monitoring method for confirming the presence of endemic catfishes in Lake Biwa. [ABSTRACT FROM AUTHOR]

Grasshoppers, as pernicious pests, pose a formidable threat to the advancement of agriculture and animal husbandry. Their presence can elicit a cascade of environmental challenges, underscoring the pressing need for effective control measures. However, grasshopper development is an intricate process influenced by diverse environmental factors with varying weights across regions, making it difficult to prevent and control. Therefore, this study focused on the prevalent infestation region, Xilingol, and selected Dasyhippus barbipes as the research subject because of its highest density and largest damaged area. Initially, according to the development mechanisms of D. barbipes, 31 habitat factors from five categories (meteorology, vegetation, soil, topography, and ecology) were selected; then, difference tests, correlation analysis, importance tests, and principal component analysis were applied to construct representative indicators for monitoring the habitat of D. barbipes (HDB). Subsequently, employing the occurrence data of D. barbipes from 2018 to 2023, a spatial pattern analysis was conducted to explore the hotspot aggregation area (HAA) and spatiotemporal characteristics of D. barbipes. Finally, considering landscape and spatial heterogeneity, the Landscape-based Geographically Weighted Logistic Regression (L-GWLR) model for HDB was constructed to achieve adaptive changes in factor weights across regions. The indicators included minimum temperature during the egg stage, precipitation, and soil temperature during the spawning stage, slope, fractional vegetation coverage in the nymph stage, soil moisture in the 1st to 3rd nymph instar, patch area, and gyration radius. The spatial pattern analysis revealed a significant spatial autocorrelation in the distribution of D. barbipes at a 90 % confidence interval (z > 1.65 and p

In accordance with the 92/43/EEC “Habitats” Directive, Mediterranean temporary ponds are identified as a priority natural habitat within the European context. They are a very interesting and unique habitat type, as ecological conditions can vary greatly in a short period of time. Due to their small size, many Mediterranean hydrophytic bryophytes typical of this habitat are often overlooked or misinterpreted. Their distribution, habitats, ecology, and strategies are generally poorly understood. Several of them are currently considered rare or endangered in the Mediterranean. As these ponds are particularly sensitive to human activities and natural changes, such bryophytes and associated vegetation communities may be at risk. This study is focused on their floristic variability in different environmental conditions in two sites of particular phytogeographic interest in the Mediterranean area. In the Sardinian Pauli of Giara, 56 taxa (50 Bryophyta and 6 Marchantiophyta) were found, and in the Umbria Piana di Ferretto, 54 taxa (34 Bryophyta and 20 Marchantiophyta) were documented. The taxa from the two areas were analysed and compared. Life strategies, life macroforms, light and moisture preferences, chorological elements, and moisture belts were considered. The data are presented here together with information on the phytogeography and ecology of the species recorded. The findings indicate that a bespoke monitoring strategy and dedicated conservation measures are essential for the effective protection of bryophytes, ensuring the achievement of meaningful and sustainable conservation outcomes.

The endangered javan gibbon (Hylobates moloch) has been threaten by massive habitat loss and fragmentation in Java. The survival of the second largest population which inhabited unprotected Dieng mountains faced greater risk to habitat conversion and fragmentation. The landscape-level habitat monitoring using spatiotemporal quantification is crucial as a baseline data for javan gibbon conservation. Here, the land-use and land-cover (LULC) change of the Javan gibbon habitat during 1994-2009-2021 and its fragmentation in the Dieng mountains were quantified. This study revealed there were no significant decline in the total of forest. However, its quality was degraded in the interior of forest block. The forest has more fragmented from large patches into smaller patches and increased forest edge. The higher fragmentation happened in the areas that traversed by road. Six suitable forest blocks were identified with varying level of connectivity. Protection and restoration both in the forest and in the interior forest is immediate need, especially in the main forest block. The extra effort is also crucial in the connected forest but traversed by road. The blocks which closely isolated by road could be potentially reconnected by artificial canopy bridge, while the other distantly isolated block might need habitat restoration for corridor. [ABSTRACT FROM AUTHOR]

The endangered javan gibbon (Hylobates moloch) has been threaten by massive habitat loss and fragmentation in Java. The survival of the second largest population which inhabited unprotected Dieng mountains faced greater risk to habitat conversion and fragmentation. The landscape-level habitat monitoring using spatiotemporal quantification is crucial as a baseline data for javan gibbon conservation. Here, the land-use and land-cover (LULC) change of the Javan gibbon habitat during 1994–2009–2021 and its fragmentation in the Dieng mountains were quantified. This study revealed there were no significant decline in the total of forest. However, its quality was degraded in the interior of forest block. The forest has more fragmented from large patches into smaller patches and increased forest edge. The higher fragmentation happened in the areas that traversed by road. Six suitable forest blocks were identified with varying level of connectivity. Protection and restoration both in the forest and in the interior forest is immediate need, especially in the main forest block. The extra effort is also crucial in the connected forest but traversed by road. The blocks which closely isolated by road could be potentially reconnected by artificial canopy bridge, while the other distantly isolated block might need habitat restoration for corridor.

Forests supply multiple ecosystem services and host a large proportion of the Earth's terrestrial biodiversity. In particular, they provide habitats for many taxonomic groups which can be threatened by forest unsustainable management practices. Type and intensity of forest management are widely recognized as the main drivers of structure and functions in forests ecosystems. However, to better understand the impacts and the benefits deriving from forest management, there is a big need to standardize procedures of field data collection and data analysis.Here, we provide a georeferenced dataset of vertical and horizontal structure of forest types belonging to 4 habitat types, sensu Council Directive 92/43/EEC. The dataset includes structural indicators commonly linked to old-growth forests in Europe, in particular the amount of standing and lying deadwood. We collected data on 32 plots (24 of 225 m2, and 8 of 100 m2, according to different forests type) during spring and summer of 2022, in Val d'Agri (Basilicata, Southern Italy). The dataset we provide follows the common national standard for field data collection in forest habitat types, published by ISPRA in 2016 with the aim to promote a greater homogeneity in assessment of habitat conservation status at Country and biogeographical level, as requested by the Habitats Directive.

Grasshoppers can greatly interfere with agriculture and husbandry, and they will breed and grow rapidly in suitable habitats. Therefore, it is necessary to extract the distribution of the grasshopper potential habitat (GPH), analyze the spatial-temporal characteristics of the GPH, and detect the different effects of key environmental factors in the meadow and typical steppe. To achieve the goal, this study took the two steppe types of Xilingol (the Inner Mongolia Autonomous Region of China) as the research object and coupled them with the MaxEnt and multisource remote sensing data to establish a model. First, the environmental factors, including meteorological, vegetation, topographic, and soil factors, that affect the developmental stages of grasshoppers were obtained. Secondly, the GPH associated with meadow and typical steppes from 2018 to 2022 were extracted based on the MaxEnt model. Then, the spatial-temporal characteristics of the GPHs were analyzed. Finally, the effects of the habitat factors in two steppe types were explored. The results demonstrated that the most suitable and moderately suitable areas were distributed mainly in the southern part of the meadow steppe and the eastern and southern parts of the typical steppe. Additionally, most areas in the town of Gaorihan, Honggeergaole, Jirengaole, as well as the border of Wulanhalage and Haoretugaole became more suitable for grasshoppers from 2018 to 2022. This paper also found that the soil temperature in the egg stage, the vegetation type, the soil type, and the precipitation amount in the nymph stage were significant factors both in the meadow and typical steppes. The slope and precipitation in the egg stage played more important roles in the typical steppe, whereas the aspect had a greater contribution to the meadow steppe. These findings can provide a methodical guide for grasshopper control and management and for further ensuring the security of agriculture and husbandry. [ABSTRACT FROM AUTHOR]

Zone Umide della Capitanata, located in the northeastern part of the Apulia Region, is one of the most extensive coastal wetlands of the Italian peninsula and one of the largest components of the Mediterranean wetland system. Despite its high ecological importance, this site has been undergoing a variety of pressures intensified in recent decades. This study analyzes and evaluates the changes occurred in this area between 2010 and 2020. Land cover and habitat maps were performed by photointerpretation and on-site surveys, and classified according to the FAO-LCCS and EUNIS taxonomies, respectively. To focus on local dynamics, four subset areas were analyzed separately. A set of landscape metrics was computed to analyze the landscape structure. The anthropogenic pressures affecting the study area were described through the Driving Forces-Pressures-State-Impact-Response (DPSIR) conceptual framework. Numerous changes were identified, deriving from both finest thematic redefinition and anthropogenic pressures. Both class conversions and class modifications were identified and quantified by means of transition matrices. Most of the observed conversions were borne by classes belonging to saltmarshes and to coastal dune systems. In particular, landscape configuration of coastal dune classes was well highlighted by a set of specific landscape metrics. Agriculture practices and changes in water flow pattern turned out to be the main driving forces exerting pressures on these natural systems. Significant differences were found between the four subsets under analysis, thus indicating that different management strategies lead to different levels of conservation. [ABSTRACT FROM AUTHOR]

Intertidal areas, which emerge during low tide, form a vital link between terrestrial and marine environments. Seagrasses, a well-studied intertidal habitat, provide a multitude of different ecosystem goods and services. However, owing to their relatively high exposure to anthropogenic impacts, seagrasss meadows and other intertidal habitats have seen extensive declines. Remote sensing methods that can capture the spatial and temporal variation of marine habitats are essential to best assess the trajectories of seagrass ecosystems. An advanced machine learning method has been developed to map intertidal vegetation from satellite-derived surface reflectance at a 12-band multispectral resolution and distinguish between similarly pigmented intertidal macrophytes, such as seagrass and green algae. The Intertidal Classification of Europe: Categorising Reflectance of Emerged Areas of Marine vegetation with Sentinel-2 (ICE CREAMS v1.0), a neural network model trained on over 300,000 Sentinel-2 pixels to identify different intertidal habitats, was applied to the open-access long term archive of systematically collected Sentinel-2 imagery to provide 7 years (2017–2023) worth of intertidal seagrass dynamics in 6 sites across Western Europe (471 Sentinel-2 Images). A combination of independently collected visually inspected Uncrewed Aerial Vehicle imagery and in situ quadrat images were used to validate ICE CREAMS. Having achieved a high seagrass classification accuracy (0.82 over 12,000 pixels) and consistent conversion into cover (19% RMSD), the ICE CREAMS model outputs provided evidence of site specific variation in trajectories of seagrass extent, when appropriate consideration of intra-annual variation has been considered. Inter-annual dynamics of sites showed some instances of consistent change, some indicated stability, while others indicated instability over time, characterised by increases and decreases across the time-series in seagrass coverage. This methological pipeline has helped to create up-to-date monitoring data that, with the planned continuation of the Sentinel missions, will allow almost real-time monitoring of these habitats into the future. This process, and the data it provides, could aid management practitioners from regional to international levels, with the ability to monitor intertidal seagrass meadows at both high spatial and temporal resolution, over continental scales. The implementation of Earth Observation for high-resolution monitoring of intertidal seagrasses could therefore allow for gap-filling seagrass datasets, and sustain specific and rapid management measures. • Intertidal seagrass can accurately be classified by Sentinel-2 (S2). • European scale intertidal seagrass meadow inter-annual variation is site specific. • Neural Network model (ICE CREAMS) created for European scale. • Trends of increasing growth of Intertidal seagrass seen across multiple sites. • Methods provide an up-to-date intertidal seagrass assessment tool. [ABSTRACT FROM AUTHOR]

Evaluating temporal trends in habitat and behavioral responses is critical for conservation, yet long‐term monitoring studies are rare. We used a 35‐year dataset (1982–2017) to assess multiscale habitat use and selection by an endangered carnivore, the ocelot (Leopardus pardalis), in South Texas, USA. We used a time series of remotely sensed imagery to map changes in availability of woody cover, habitat critical to ocelots that has diminished due to anthropogenic development and increased road infrastructure. Our objectives were to characterize habitat relationships, predict high‐quality habitat, and assess behavior with changing environmental conditions. We fit functional response (third order) and individual‐specific resource selection (second order) functions to assess multiscale habitat use of vegetation cover and roads. Within home ranges, ocelots used woody cover greater than availability. Ocelots used areas near roads in proportion to availability, with minor exceptions. We observed changes in habitat use by ocelots across time with higher proportions of woody and non‐woody cover used in later time periods. Average availability of woody cover decreased in the study area between the 1980s and 2010s (0.44 in 1985 to 0.39 in 2015, p < 0.001), and ocelots used areas with a higher proportion of woody cover (≥0.48) farther from high‐traffic roads compared to availability. High‐quality ocelot habitat was consistently predicted in areas with high proportions of woody cover, while areas closer to high‐traffic roads were consistently predicted as non‐habitat. The extent of predicted habitat never exceeded 47% (1515 km2) of the study area, illustrating the confined nature of ocelot habitat. Our assessment of multiscale habitat use demonstrated that higher order selection processes likely truncate resource gradients within home ranges. Ocelots did not avoid roads as expected within home ranges, which is a likely mechanism for vehicle‐induced mortality. Private lands contained ≥79% of predicted high‐quality habitat over time. Therefore, the future of ocelots in the United States relies on private land stewardship. Insights gained from these analyses can advance habitat conservation and mitigation of road mortality for ocelot populations. [ABSTRACT FROM AUTHOR]

Abstract Evaluating temporal trends in habitat and behavioral responses is critical for conservation, yet long‐term monitoring studies are rare. We used a 35‐year dataset (1982–2017) to assess multiscale habitat use and selection by an endangered carnivore, the ocelot (Leopardus pardalis), in South Texas, USA. We used a time series of remotely sensed imagery to map changes in availability of woody cover, habitat critical to ocelots that has diminished due to anthropogenic development and increased road infrastructure. Our objectives were to characterize habitat relationships, predict high‐quality habitat, and assess behavior with changing environmental conditions. We fit functional response (third order) and individual‐specific resource selection (second order) functions to assess multiscale habitat use of vegetation cover and roads. Within home ranges, ocelots used woody cover greater than availability. Ocelots used areas near roads in proportion to availability, with minor exceptions. We observed changes in habitat use by ocelots across time with higher proportions of woody and non‐woody cover used in later time periods. Average availability of woody cover decreased in the study area between the 1980s and 2010s (0.44 in 1985 to 0.39 in 2015, p

Monitoring and preserving natural habitats has become an essential activity in many countries today. As a native tree species in Korea, Paulownia coreana has periodically been surveyed in national ecological surveys and was identified as an important target for conservation as well as habitat monitoring and management. This study explores habitat suitability models (HSMs) for Paulownia coreana in conjunction with national ecological survey data and various environmental factors. Together with environmental variables, the national ecological survey data were run through machine learning algorithms such as Artificial Neural Network and Decision Tree & Rules, which were used to identify the impact of individual variables and create HSMs for Paulownia coreana, respectively. Unlike other studies, which used remote sensing data to create HSMs, this study employed periodical on-site survey data for enhanced validity. Moreover, localized environmental resources such as topography, soil, and rainfall were taken into account to project habitat suitability. Among the environment variables used, the study identified critical attributes that affect the habitat conditions of Paulownia coreana. Therefore, the habitat suitability modelling methods employed in this study could play key roles in planning, monitoring, and managing plants species in regional and national levels. Furthermore, it could shed light on existing challenges and future research needs. [ABSTRACT FROM AUTHOR]

Grasshoppers can greatly interfere with agriculture and husbandry, and they will breed and grow rapidly in suitable habitats. Therefore, it is necessary to extract the distribution of the grasshopper potential habitat (GPH), analyze the spatial-temporal characteristics of the GPH, and detect the different effects of key environmental factors in the meadow and typical steppe. To achieve the goal, this study took the two steppe types of Xilingol (the Inner Mongolia Autonomous Region of China) as the research object and coupled them with the MaxEnt and multisource remote sensing data to establish a model. First, the environmental factors, including meteorological, vegetation, topographic, and soil factors, that affect the developmental stages of grasshoppers were obtained. Secondly, the GPH associated with meadow and typical steppes from 2018 to 2022 were extracted based on the MaxEnt model. Then, the spatial-temporal characteristics of the GPHs were analyzed. Finally, the effects of the habitat factors in two steppe types were explored. The results demonstrated that the most suitable and moderately suitable areas were distributed mainly in the southern part of the meadow steppe and the eastern and southern parts of the typical steppe. Additionally, most areas in the town of Gaorihan, Honggeergaole, Jirengaole, as well as the border of Wulanhalage and Haoretugaole became more suitable for grasshoppers from 2018 to 2022. This paper also found that the soil temperature in the egg stage, the vegetation type, the soil type, and the precipitation amount in the nymph stage were significant factors both in the meadow and typical steppes. The slope and precipitation in the egg stage played more important roles in the typical steppe, whereas the aspect had a greater contribution to the meadow steppe. These findings can provide a methodical guide for grasshopper control and management and for further ensuring the security of agriculture and husbandry.

Wireless sensor network (WSN) has been widely deployed in precision agriculture to improve the crop production. However, we still face many challenges for large-scale farmland habitat monitoring, the energy shortage for battery-powered sensor nodes, the complicated propagation environment for wireless signals during different growing-up periods of the crops, the optimization of the coverage of the sensor nodes, etc. In order to guarantee the holeless coverage of the sensor nodes during the whole life of the crops, the WSNs are typically deployed with high density. Therefore, some of the sensors in the network are redundant in certain growing-up periods of the crops. And also the data collected by each sensor node may have strong temporal correlation. Recently, compressive sensing (CS) has received much attention due to its capability of reconstructing sparse signals with the number of measurements much lower than that of the Nyquist sampling rate. With the rapid progress of CS based sparse representation, matrix completion (MC) theory was proposed very recently. According to the MC theory, a low-rank matrix can be accurately rebuilt with a few number of entries in the matrix. Matrix completion provides the advantage of sampling small set of data at sensor nodes without requiring excessive computational and traffic loads, which meets the requirement of energy-efficient data gathering and transmitting in WSNs. In this research, by considering the characteristics that the sensor nodes are redundant in some growing-up periods of the crops and the data collected by sensor-nodes usually share a strong spatial and temporal correlation among them in WSNs for large-scale farmland habitat monitoring, we put forward a MC based two-step energy saving optimization algorithm to reduce both the energy consumption of the data acquisition and data transmission process in WSNs and achieved the purpose of prolonging the network lifetime. Firstly, through the measurement of the sensor node's data information, we found the non-redundant nodes by considering the spatial correlation of the data from the sensor nodes. We would close the data acquisition units of the remaining redundant nodes and make them only transmit data as relay nodes. Secondly, we took advantage of the partial sampling scheme in matrix completion to further reduce the quantity of data. Thereby, we could reduce the energy consumption on both data collection and transmission process of the wireless sensor network. The experiment results show that the proposed algorithm reduces 83% working nodes in the network, and therefore reducing the energy cost of the network.

Disturbances play a key role in driving forest ecosystem dynamics, but how disturbances shape wildlife habitat across space and time often remains unclear. A major reason for this is a lack of information about changes in habitat suitability across large areas and longer time periods. Here, we use a novel approach based on Landsat satellite image time series to map seasonal habitat suitability annually from 1986 to 2017. Our approach involves characterizing forest disturbance dynamics using Landsat‐based metrics, harmonizing these metrics through a temporal segmentation algorithm, and then using them together with GPS telemetry data in habitat models. We apply this framework to assess how natural forest disturbances and post‐disturbance salvage logging affect habitat suitability for two ungulates, roe deer (Capreolus capreolus) and red deer (Cervus elaphus), over 32 yr in a Central European forest landscape. We found that red and roe deer differed in their response to forest disturbances. Habitat suitability for red deer consistently improved after disturbances, whereas the suitability of disturbed sites was more variable for roe deer depending on season (lower during winter than summer) and disturbance agent (lower in windthrow vs. bark‐beetle‐affected stands). Salvage logging altered the suitability of bark beetle‐affected stands for deer, having negative effects on red deer and mixed effects on roe deer, but generally did not have clear effects on habitat suitability in windthrows. Our results highlight long‐lasting legacy effects of forest disturbances on deer habitat. For example, bark beetle disturbances improved red deer habitat suitability for at least 25 yr. The duration of disturbance impacts generally increased with elevation. Methodologically, our approach proved effective for improving the robustness of habitat reconstructions from Landsat time series: integrating multiyear telemetry data into single, multi‐temporal habitat models improved model transferability in time. Likewise, temporally segmenting the Landsat‐based metrics increased the temporal consistency of our habitat suitability maps. As the frequency of natural forest disturbances is increasing across the globe, their impacts on wildlife habitat should be considered in wildlife and forest management. Our approach offers a widely applicable method for monitoring habitat suitability changes caused by landscape dynamics such as forest disturbance. [ABSTRACT FROM AUTHOR]

Monitoring and preserving natural habitats has become an essential activity in many countries today. As a native tree species in Korea, Paulownia coreana has periodically been surveyed in national ecological surveys and was identified as an important target for conservation as well as habitat monitoring and management. This study explores habitat suitability models (HSMs) for Paulownia coreana in conjunction with national ecological survey data and various environmental factors. Together with environmental variables, the national ecological survey data were run through machine learning algorithms such as Artificial Neural Network and Decision Tree & Rules, which were used to identify the impact of individual variables and create HSMs for Paulownia coreana, respectively. Unlike other studies, which used remote sensing data to create HSMs, this study employed periodical on-site survey data for enhanced validity. Moreover, localized environmental resources such as topography, soil, and rainfall were taken into account to project habitat suitability. Among the environment variables used, the study identified critical attributes that affect the habitat conditions of Paulownia coreana. Therefore, the habitat suitability modelling methods employed in this study could play key roles in planning, monitoring, and managing plants species in regional and national levels. Furthermore, it could shed light on existing challenges and future research needs.

Question: Does spectral diversity captured by unmanned aerial systems (UAS) provide reliable information for monitoring the eco‐geomorphological integrity of Mediterranean coastal dune ecosystems? Can this information discriminate between two coastal areas with low (LP) and high (HP) human pressure? Location: Tyrrhenian coast, Central Italy. Methods: By processing UAS images, we derived the normalized difference vegetation index (NDVI) and topographic variables at high spatial resolution (0.5 m) for 150 m wide strips starting from the coastline inland on two representative coastal tracts under low and high human pressure. We mapped the sea–inland heterogeneity applying Rao's Q index to the plant biomass (NDVI) and geomorphology variables (elevation and slope). Since Rao's Q index can be calculated in a multidimensional space, we summarized the variability of these three variables into a single eco‐geomorphological layer. We then inspected and compared how the Rao's Q index values for plant biomass, geomorphology and eco‐geomorphology change as a function of the distance from the sea between the two coastal sites. Results: Rao's Q heterogeneity values vary along the sea–inland gradient of well‐preserved sites (LP). The maximum eco‐geomorphological heterogeneity was found at intermediate distances from the sea and decreased toward the inner sector where the dune geomorphology was more stable and vegetation more homogeneously distributed. Instead, Rao's Q heterogeneity values featured constant low values along the gradient on the HP site, highlighting a simplified eco‐geomorphological gradient related to the high human pressure. Conclusions: Using UAS, the eco‐geomorphological gradient of coastal dunes can be quantified at a very fine spatial resolution over management‐relevant extents. Rao's Q index applied to sensing imagery successfully captured the differences in the eco‐geomorphological heterogeneity along the sea–inland dune gradient and among sites with different levels of anthropic pressure. This approach supports frequent surveys and is particularly suitable for spatial monitoring of key coastal functions and services. [ABSTRACT FROM AUTHOR]

There is strong evidence that a major driver of the decline of eastern North American monarch butterflies (Danaus plexippus) is the loss of breeding habitat in the upper midwestern United States. Grasslands, including conservation areas, provide some of the largest remaining tracts of breeding habitat available to monarchs. While grassland conservation has been well-studied, little is known about how monarchs interact with these areas, or how planting and management practices impact the quality of habitat for monarchs. Here, we evaluate monarch habitat and use by monarchs in 61 conservation grasslands (including restoration sites in the U.S. Department of Agriculture Conservation Reserve Program, U.S. Fish and Wildlife Service Partners for Fish and Wildlife Program, and privately funded restored prairies) in Minnesota, Wisconsin, and Iowa. We documented milkweed (Asclepias spp.) density and diversity, blooming plant frequency and richness, and immature monarch density during the monarch breeding seasons of 2016 and 2017, along with seeding and management histories. Milkweed was observed at 60 of 61 study sites with a mean density of 1,390 plants per hectare (median = 783), a greater density than previously estimated in conservation grasslands. Monarchs were observed at 57 of 61 sites. Asclepias syriaca was the most frequently observed species, regardless of whether it was planted. Asclepias tuberosa and Asclepias incarnata may be the most cost-effective milkweeds to seed in our study geography, given that they were both more likely to be present and occurred at higher densities when planted than when not planted. Forb establishment rate varied across species planted and seeding rates. Increased rates of forb establishment were observed at larger sites, sites planted in the fall, and sites with fewer species in the seed mix. We observed a relatively low frequency of early season nectar sources, suggesting that managers should consider including more early blooming species in seed mixes and on existing conservation lands. We present establishment information for consideration in seed mix design and describe how our findings can be used to inform monarch habitat availability models, future studies, and conservation efforts.

Anthropogenic climate and land use change is causing rapid shifts in the distribution and composition of habitats with profound impacts on ecosystem biodiversity. The sustainable management of ecosystems requires monitoring programmes capable of detecting shifts in habitat distribution and composition at large spatial scales. Remote sensing observations facilitate such efforts as they enable cost-efficient modelling approaches that utilize publicly available datasets and can assess the status of habitats over extended periods of time. In this study, we introduce a modelling framework for habitat monitoring in Germany using readily available MODIS surface reflectance data. We developed supervised classification models that allocate (semi-)natural areas to one of 18 classes based on their similarity to Natura 2000 habitat types. Three machine learning classifiers, i.e., Support Vector Machines (SVM), Random Forests (RF), and C5.0, and an ensemble approach were employed to predict habitat type using spectral signatures from MODIS in the visible-to-near-infrared and short-wave infrared. The models were trained on homogenous Special Areas of Conservation that are predominantly covered by a single habitat type with reference data from 2013, 2014, and 2016 and tested against ground truth data from 2010 and 2019 for independent model validation. Individually, the SVM and RF methods achieved better overall classification accuracies (SVM: 0.72–0.93%, RF: 0.72–0.94%) than the C5.0 algorithm (0.66–0.93%), while the ensemble classifier developed from the individual models gave the best performance with overall accuracies of 94.23% for 2010 and 80.34% for 2019 and also allowed a robust detection of non-classifiable pixels. We detected strong variability in the cover of individual habitat types, which were reduced when aggregated based on their similarity. Our methodology is capable to provide quantitative information on the spatial distribution of habitats, differentiate between disturbance events and gradual shifts in ecosystem composition, and could successfully allocate natural areas to Natura 2000 habitat types.

Despite growing interest in using lightweight unmanned aerial systems (UASs) for ecological research and conservation, review of the operational aspects of these evolving technologies is limited in the scientific literature. To derive an objective framework for choosing among technologies we calculated efficiency measures and conducted a data envelopment productivity frontier analysis (DEA) to compare the efficacy of using manned aircraft (Cessna with Aviatrix triggered image capture using a 50 mm lens) and UAS (Mavic Pro 2) for photogrammetric monitoring of restoration efforts in dynamic braided rivers in Southern New Zealand. Efficacy assessment was based on the technological, logistical, administrative, and economic requirements of pre (planning), peri (image acquiring) and post (image processing) phases. The results reveal that the technological and logistic aspects of UASs were more efficient than manned aircraft flights. Administratively, the first deployment of UASs is less efficient but was very flexible for subsequent deployment. Manned aircraft flights were more productive in terms of the number of acquired images, but the ground resolution of those images was lower compared with those from UASs. Frontier analysis confirmed that UASs would be economical for regular monitoring of habitats—and even more so if research personnel are trained to fly the UASs.

In the light of the “Biological Diversity” concept, habitats are cardinal pieces for biodiversity quantitative estimation at a local and global scale. In Europe EUNIS (European Nature Information System) is a system tool for habitat identification and assessment. Earth Observation (EO) data, which are acquired by satellite sensors, offer new opportunities for environmental sciences and they are revolutionizing the methodologies applied. These are providing unprecedented insights for habitat monitoring and for evaluating the Sustainable Development Goals (SDGs) indicators. This paper shows the results of a novel approach for a spatially explicit habitat mapping in Italy at a national scale, using a supervised machine learning model (SMLM), through the combination of vegetation plot database (as response variable), and both spectral and environmental predictors. The procedure integrates forest habitat data in Italy from the European Vegetation Archive (EVA), with Sentinel-2 imagery processing (vegetation indices time series, spectral indices, and single bands spectral signals) and environmental data variables (i.e., climatic and topographic), to parameterize a Random Forests (RF) classifier. The obtained results classify 24 forest habitats according to the EUNIS III level: 12 broadleaved deciduous (T1), 4 broadleaved evergreen (T2) and eight needleleaved forest habitats (T3), and achieved an overall accuracy of 87% at the EUNIS II level classes (T1, T2, T3), and an overall accuracy of 76.14% at the EUNIS III level. The highest overall accuracy value was obtained for the broadleaved evergreen forest equal to 91%, followed by 76% and 68% for needleleaved and broadleaved deciduous habitat forests, respectively. The results of the proposed methodology open the way to increase the EUNIS habitat categories to be mapped together with their geographical extent, and to test different semi-supervised machine learning algorithms and ensemble modelling methods.

Several authors have shown that the sounds of anurans can be used as an indicator of climate change. Hence, the recording, storage and further processing of a huge number of anuran sounds, distributed over time and space, are required in order to obtain this indicator. Furthermore, it is desirable to have algorithms and tools for the automatic classification of the different classes of sounds. In this paper, six classification methods are proposed, all based on the data-mining domain, which strive to take advantage of the temporal character of the sounds. The definition and comparison of these classification methods is undertaken using several approaches. The main conclusions of this paper are that: (i) the sliding window method attained the best results in the experiments presented, and even outperformed the hidden Markov models usually employed in similar applications; (ii) noteworthy overall classification performance has been obtained, which is an especially striking result considering that the sounds analysed were affected by a highly noisy background; (iii) the instance selection for the determination of the sounds in the training dataset offers better results than cross-validation techniques; and (iv) the temporally-aware classifiers have revealed that they can obtain better performance than their non-temporally-aware counterparts. [ABSTRACT FROM AUTHOR]

Several authors have shown that the sounds of anurans can be used as an indicator of climate change. Hence, the recording, storage and further processing of a huge number of anuran sounds, distributed over time and space, are required in order to obtain this indicator. Furthermore, it is desirable to have algorithms and tools for the automatic classification of the different classes of sounds. In this paper, six classification methods are proposed, all based on the data-mining domain, which strive to take advantage of the temporal character of the sounds. The definition and comparison of these classification methods is undertaken using several approaches. The main conclusions of this paper are that: (i) the sliding window method attained the best results in the experiments presented, and even outperformed the hidden Markov models usually employed in similar applications; (ii) noteworthy overall classification performance has been obtained, which is an especially striking result considering that the sounds analysed were affected by a highly noisy background; (iii) the instance selection for the determination of the sounds in the training dataset offers better results than cross-validation techniques; and (iv) the temporally-aware classifiers have revealed that they can obtain better performance than their non-temporally-aware counterparts.

The conservation of semi-natural habitats represents a primary challenge for European nature conservation due to their great species diversity and their vulnerability to ongoing massive land-use changes. As these changes rapidly transform and phase out semi-natural habitats, conservation measures should be prompt and specifically focused on a sound assessment of the degree of conservation. Here we develop a methodological strategy for the assessment of the degree of conservation of semi-natural grasslands based on well-defined criteria rather than on expert opinion. Through mixed effect models, we tested ten potential indicators, encompassing proxies of species composition, habitat structure, and landscape patterns, against a measure of compositional change from habitat favourable condition, i.e., an inverse proxy of conservation status. This measure derives from the re-visitation of 132 sampling units historically sampled between 1966 and 1992 along the Apennines. The compositional change was quantified as the dissimilarity between historical habitat species pools and the composition of current communities. The compositional change was significantly related to the number of habitat diagnostic species and the relative cover of woody species with opposite sign (positive and negative, respectively). We classified and combined the classes of these two indicators in each sampling unit to assess the habitat degree of conservation at the plot and at the Natura 2000 site level. At the plot level, our assessment was in good agreement with the occurrence of species of conservation concern. On the other hand, at the site level, our assessment was not always harmonic with the habitat conservation assessment officially reported for the site investigated. [ABSTRACT FROM AUTHOR]

Several authors have shown that the sounds of anurans can be used as an indicator of climate change. Hence, the recording, storage and further processing of a huge number of anuran sounds, distributed over time and space, are required in order to obtain this indicator. Furthermore, it is desirable to have algorithms and tools for the automatic classification of the different classes of sounds. In this paper, six classification methods are proposed, all based on the data-mining domain, which strive to take advantage of the temporal character of the sounds. The definition and comparison of these classification methods is undertaken using several approaches. The main conclusions of this paper are that: (i) the sliding window method attained the best results in the experiments presented, and even outperformed the hidden Markov models usually employed in similar applications; (ii) noteworthy overall classification performance has been obtained, which is an especially striking result considering that the sounds analysed were affected by a highly noisy background; (iii) the instance selection for the determination of the sounds in the training dataset offers better results than cross-validation techniques; and (iv) the temporally-aware classifiers have revealed that they can obtain better performance than their non-temporally-aware counterparts. [ABSTRACT FROM AUTHOR]

Coastal plant communities are being transformed or lost because of sea level rise (SLR) and land-use change. In conjunction with SLR, the Florida Everglades ecosystem has undergone large-scale drainage and restoration, altering coastal vegetation throughout south Florida. To understand how coastal plant communities are changing over time, accurate mapping techniques are needed that can define plant communities at a fine-enough resolution to detect fine-scale changes. We explored using bi-seasonal versus single-season WorldView-2 satellite data to map three mangrove and four adjacent plant communities, including the buttonwood/glycophyte community that harbors the federally-endangered plant Chromolaena frustrata. Bi-seasonal dataweremore effective than single-season to differentiate all communities of interest. Bi-seasonal data combined with Light Detection and Ranging (LiDAR) elevation datawere used tomap coastal plant communities of a coastal stretchwithin Everglades National Park (ENP). Overall map accuracy was 86%. Black and red mangroves were the dominant communities and covered 50% of the study site. All the remaining communities had ≤10% cover, including the buttonwood/glycophyte community. ENP harbors 21 rare coastal species threatened by SLR. The spatially explicit, quantitative data provided by our map provides a fine-scale baseline for monitoring future change in these species’ habitats. Our results also offer a method to monitor vegetation change in other threatened habitats. [ABSTRACT FROM AUTHOR]

This study utilizes repeated geoacoustic mapping to quantify the morphodynamic response of the nearshore to storm-induced changes. The aim of this study was to quantitatively map the nearshore zone of Assateague Island National Seashore (ASIS) to determine what changes in bottom geomorphology and benthic habitats are attributable to storm events including hurricane Sandy and the passage of hurricane Joaquin. Specifically, (1) the entire domain of the National Parks Service offshore area was mapped with side-scan sonar and multibeam bathymetry at a resolution comparable to that of the existing pre-storm survey, (2) a subset of the benthic stations were resampled that represented all sediment strata previously identified, and (3) newly obtained data were compared to that from the pre-storm survey to determined changes that could be attributed to specific storms such as Sandy and Joaquin. Capturing event specific dynamics requires rapid response surveys in close temporal association of the before and after period. The time-lapse between the pre-storm surveys for Sandy and our study meant that only a time and storm integrated signature for that storm could be obtained whereas with hurricane Joaquin we could identify impacts to the habitat type and geomorphology more directly related to that particular storm. This storm impacts study provides for the National Park Service direct documentation of storm-related changes in sediments and marine habitats on multiple scales: From large scale, side-scan sonar maps and interpretation of acoustic bottom types, to characterize as fully as possible habitats from 1 to 10 m up to many kilometer scales, as well as from point benthic samples within each sediment stratum and these results can help guide management of the island resources.

The quantitative characterization of landscape structure is critical to assess conservation, and monitor and manage biodiversity. The Mediterranean Basin is a biodiversity hotspot that illustrates the strong relationship between biodiversity and the complexity of the landscape mosaic. Our objective was to test the relevance of two textural indices and one radiometric index (the normalized difference vegetation index (NDVI)) to characterize vegetation structure. These indices could be used as indicators of vegetation composition and organization of four vertical strata when derived from airborne and Pléiades space-borne VHSR imagery. More specifically, we analyzed the influence of the spatial resolution and the radiometric information on the characterization of the landscape structure. Our results indicated that NDVI information at 0.5 m spatial resolution was necessary to be able to incorporate the heterogeneity of vegetation structure. Indices derived from lower resolution NDVI images or different radiometric information than airborne images also proved to be sensitive to vegetation fragmentation and composition. NDVI images brought out details on ligneous/herbs patterns while panchromatic image brought out more details on herbs/bare soil patterns. Combined textural and NDVI indices show strong potential for vegetation structure understanding, allowing detailed mapping. NDVI information shows good potential for applications related to landscape closure dynamics; related habitat degradation indicators caused by shrub encroachment. Panchromatic derived information, on the other hand, provides information relevant in applications focusing grazing management.

Habitat-based conservation is a priority for the endangered American burying beetle ( Nicrophorus americanus Olivier), but its annual life history, dispersal ability, and low detectability contribute to difficulties explaining its decline, identifying manageable habitat characteristics, and mapping its distribution., Objectives of this study were to (i) assess factors affecting detection of beetles with baited pitfall traps, (ii) identify important habitat characteristics, and (iii) map spatio-temporal patterns of habitat quality and beetle abundance., At a military installation in western Arkansas with one of the largest remaining N. americanus populations, 257 sites were surveyed over 5 years using three nights of baited pitfall trapping at each site., Geographic information systems (GIS)-based habitat assessments included three site radii (100, 800, and 1600 m). Year-specific vegetation condition was quantified using satellite images and other GIS data., N-mixture models were used to account for imperfect detection and to assess competing models of beetle abundance and detection., Detection rates (mean = 20%) were dependent on overnight temperature, dew point, and wind speed with upper and lower temperature thresholds beyond which detection was reduced., Habitat assessments were most effective within 800 m site radii, consistent with previous estimates of N. americanus trap sample range., Beetle abundance was associated with grasslands and open-canopy woodlands with rolling topography, sandy loam soils, and moderate patchy disturbances from wildfire., Landsat, unlike vegetation maps, quantified year-specific habitat quality and allowed spatio-temporal population dynamics to be mapped at a yearly time-step for this annual species. [ABSTRACT FROM AUTHOR]

The goal of salmonid habitat monitoring programs is to measure habitat attributes linked to salmonid productivity based on protocols that have sufficient precision to detect environmental variation at relevant spatial and temporal scales. Benthic macroinvertebrate community composition often is evaluated as part of habitat monitoring and assessment protocols, despite a lack of direct relationships between benthic composition and salmonid production. Macroinvertebrate drift provides a direct measure of the food resources available to stream salmonids, but drift is rarely evaluated as part of habitat monitoring protocols. This reluctance may stem from the complex spatial and temporal variability inherent in macroinvertebrate drift abundances and an assumed inability to obtain precise estimates of drift abundance at relevant spatial and temporal scales. We evaluated an extensive set of paired drift and benthic macroinvertebrate samples to characterize variation in the biomass and density (i.e., counts) of macroinvertebrate samples across a hierarchy of spatial and temporal scales. Results suggest that estimates of total drift biomass may offer the most precise approach for detecting differences in salmonid food availability among stream reaches and, thus, may be more appropriate than benthic sampling for incorporation into salmonid habitat monitoring programs. [ABSTRACT FROM AUTHOR]

Monitoring is one of the key tools employed to help understand the condition of the natural environment and inform the development of appropriate management actions. While international conventions encourage the use of standardised methods, the link between the information monitoring provides and local management needs is frequently overlooked. This problem is further exacerbated when monitoring is employed in areas where there are divergent interests among stakeholders in land use and management. Such problems are found in the management of wild deer across Scotland, where monitoring, in the form of habitat impact assessments, have been introduced as an innovation in sustainable deer management. However, the uptake of habitat impact assessments has been limited. We used deer management in Scotland as a case study to explore whether reinventing habitat impact assessments, and hosting the system on a familiar digital platform (a mobile phone) could help to remove perceived barriers to the implementation of assessments. Using the diffusion of innovations as a theoretical framework three sets of workshops were conducted with participants representing different stakeholder interests. While the proposed digital system did address perceived barriers to the conduct of habitat monitoring, in addition it revealed underlying concerns on the use and purpose of habitat monitoring as a tool in land management. Such concerns indicate friction between scientific and management perspectives, which need to be considered and addressed if monitoring is to become more widely acceptable as a tool to inform the management of natural resources. [ABSTRACT FROM AUTHOR]

Wireless Sensor Networks (WSNs) are a technology that is becoming very popular for many applications, and environmental monitoring is one of its most important application areas. This technology solves the lack of flexibility of wired sensor installations and, at the same time, reduces the deployment costs. To demonstrate the advantages of WSN technology, for the last five years we have been deploying some prototypes in the Doñana Biological Reserve, which is an important protected area in Southern Spain. These prototypes not only evaluate the technology, but also solve some of the monitoring problems that have been raised by biologists working in Doñana. This paper presents a review of the work that has been developed during these five years. Here, we demonstrate the enormous potential of using machine learning in wireless sensor networks for environmental and animal monitoring because this approach increases the amount of useful information and reduces the effort that is required by biologists in an environmental monitoring task.

This paper proposes a novel and autonomous weighing system for wild animals. It allows evaluating changes in the body weight of animals in their natural environment without causing stress. The proposed system comprises a smart scale designed to estimate individual body weights and their temporal evolution in a bird colony. The system is based on computational intelligence, and offers valuable large amount of data to evaluate the relationship between long-term changes in the behavior of individuals and global change. The real deployment of this system has been for monitoring a breeding colony of lesser kestrels (Falco naumanni) in southern Spain. The results show that it is possible to monitor individual weight changes during the breeding season and to compare the weight evolution in males and females.

The reduction in size, power consumption and price of many sensor devices has enabled the deployment of many sensor networks that can be used to monitor and control several aspects of various habitats. More specifically, the analysis of sounds has attracted a huge interest in urban and wildlife environments where the classification of the different signals has become a major issue. Various algorithms have been described for this purpose, a number of which frame the sound and classify these frames, while others take advantage of the sequential information embedded in a sound signal. In the paper, a new algorithm is proposed that, while maintaining the frame-classification advantages, adds a new phase that considers and classifies the score series derived after frame labelling. These score series are represented using cepstral coefficients and classified using standard machine-learning classifiers. The proposed algorithm has been applied to a dataset of anuran calls and its results compared to the performance obtained in previous experiments on sensor networks. The main outcome of our research is that the consideration of score series strongly outperforms other algorithms and attains outstanding performance despite the noisy background commonly encountered in this kind of application.

Remote sensing tools (RS) can contribute to a better understanding of the diversity of natural and semi-naturals habitats, their spatial distribution, and their conservation status. RS can also provide a generic set of derived indicators to support local to regional habitat monitoring. Here we propose a set of synthetic continuous textural indices computed from high spatial resolution airborne images for the characterization of vegetation structure in very heterogeneous landscape mosaics. These indices are based on Fourier-based textural ordination (FOTO) of very-high-resolution images. We investigate the relationship between textural indices and a set of common landscape metrics derived from vegetation maps, identifying four strata of interest: bare soil, herbs, low ligneous, and high ligneous. We identify two continuous textural indices, the first one being related to vegetation strata fragmentation and the second being related to the dominance of high ligneous. The combination of these two textural indices with the Normalized Difference Vegetation Index (NDVI) provides a synoptic and accurate overview of the spatial organization of the different vegetation strata. The methodological approach presented herein has a generic value in response to national conservation targets in the context of mapping relevant habitat indicators.