Source Of Spatial Information Research Articles

Landmarks, beacons, or panoramic views: What do pigeons attend to for guidance in familiar environments?

Birds and social insects represent excellent systems for understanding visually guided navigation. Both animal groups use surrounding visual cues for homing and foraging. Ants extract sufficient spatial information from panoramic views, which naturally embed all near and far spatial information, for successful homing. Although egocentric panoramic views allow for parsimonious explanations of navigational behaviors, this potential source of spatial information has been mostly neglected during studies of vertebrates. Here we investigate how distinct landmarks, a beacon, and panoramic views influence the reorientation behavior in pigeons (Columba livia). Pigeons were trained to search for a location characterized by a beacon and several distinct landmarks. Transformation tests manipulated aspects of the landmark configuration, allowing for a dissociation among navigational strategies. Quantitative image and path analyses provided support that the panoramic view was used by the pigeons. Although the results from some individuals support the use of beaconing, overall the pigeons relied predominantly on the panoramic view when spatial cues provided conflicting information regarding the goal location. Reorientation based on vector and bearing information derived from distinct landmarks as well as environmental geometry failed to account fully for the results. Thus, the results of our study support that pigeons can use panoramic views for reorientation in familiar environments. Given that the current model for landmark use by pigeons posits the use of different vectors from an object, a global panorama-matching strategy suggests a fundamental change in the theory of how pigeons use surrounding visual cues for localization.

Identifying the Spatial Heterogeneity and Driving Factors of Satellite-Based and Hydrologically Modeled Profile Soil Moisture

Profile soil moisture (PSM), the soil water content in the whole soil layer, directly controls the major processes related to biological interaction, vegetation growth, and runoff generation. Its spatial heterogeneity, which refers to the uneven distribution and complexity in space, influences refined spatial management and decision-making in ecological, agricultural, and hydrological systems. Satellite instruments and hydrological models are two important sources of spatial information on PSM, but there is still a gap in understanding their potential mechanisms that affect spatial heterogeneity. This study is designed to identify the spatial heterogeneity and the driving factors of two PSM datasets; one is preprocessed from a satellite product (European Space Agency Climate Change Initiative, ESA CCI), and the other is simulated from a distributed hydrological model (the DEM-based distributed rainfall-runoff model, DDRM). Three catchments with different climate conditions were chosen as the study area. By considering the scale dependence of spatial heterogeneity, the profile saturation degree (PSD) datasets from different sources (shown as ESA CCI PSD and DDRM PSD, respectively) during 2017 that are matched in terms of spatial scale and physical properties were acquired first based on the calibration data from 2014–2016, and then the spatial heterogeneity of the PSD from different sources was identified by using spatial statistical analysis and the semi-variogram method, followed by the geographic detector method, to investigate the driving factors. The results indicate that (1) ESA CCI and DDRM PSD are similar for seasonal changes and are overall consistent and locally different in terms of the spatial variations in catchment with different climate conditions; (2) based on spatial statistical analysis, the spatial heterogeneity of PSD reduces after spatial rescaling; at the same spatial scale, DDRM PSD shows higher spatial heterogeneity than ESA CCI PSD, and the low-flow period shows higher spatial heterogeneity than the high-flow period; (3) based on the semi-variogram method, both ESA CCI and DDRM PSD show strong spatial heterogeneity in most cases, in which the proportion of C/(C0 + C) is higher than 0.75, and the spatial data in the low-flow period mostly show larger spatial heterogeneity, in which the proportion is higher than 0.9; the spatial heterogeneity of PSD is higher in the semi-arid catchment; (4) the first three driving factors of the spatial heterogeneity of both ESA CCI and DDRM PSD are DEM, precipitation, and soil type in most cases, contributing more than 50% to spatial heterogeneity; (5) precipitation contributes most to ESA CCI PSD in the low-flow period, and there is no obvious high contribution of precipitation to DDRM PSD. The research provides insights into the spatial heterogeneity of PSM, which helps develop refined modeling and spatial management strategies for soil moisture in ecological, agricultural, and hydrological fields.

Get Spatial from Non-Spatial Information: Inferring Spatial Information from Textual Descriptions by Conceptual Spaces

With the rapid growth of social media, textual content is increasingly growing. Unstructured texts are a rich source of latent spatial information. Extracting such information is useful in query processing, geographical information retrieval (GIR), and recommender systems. In this paper, we propose a novel approach to infer spatial information from salient features of non-spatial nature in text corpora. We propose two methods, namely DCS and RCS, to represent place-based concepts. In addition, two measures, namely the Shannon entropy and the Moran’s I, are proposed to calculate the degree of geo-indicativeness of terms in texts. The methodology is compared with a Latent Dirichlet Allocation (LDA) approach to estimate the accuracy improvement. We evaluated the methods on a dataset of rental property advertisements in Iran and a dataset of Persian Wikipedia articles. The results show that our proposed approach enhances the relative accuracy of predictions by about 10% in case of the renting advertisements and by 13% in case of the Wikipedia articles. The average distance error is about 13.3 km for the advertisements and 10.3 km for the Wikipedia articles, making the method suitable to infer the general region of the city in which a property is located. The proposed methodology is promising for inferring spatial knowledge from textual content that lacks spatial terms.

Remote Sensing and GIS Applications in Soil Salinity Analysis: A Comprehensive Review

Soil salinity is a pressing global issue with far-reaching implications for agricultural productivity and environmental sustainability, particularly in arid and semi-arid regions. The expansion of cultivated lands and the need for food production have intensified the challenges associated with soil salinization. This paper reviews the significance of monitoring and assessing soil salinity, especially in regions where traditional irrigation practices and inadequate drainage systems exacerbate the problem. The paper highlights the importance of satellite-based technologies for spatial and temporal mapping of soil salinity, providing cost-effective, rapid, and efficient sources of qualitative and quantitative spatial information. Multispectral remote sensing data have significantly improved the monitoring of soil salinity. The spectral characteristics of salt-affected soil, visible and near-infrared bands, enable the detection of salinity in both barren and vegetated areas. Various salinity and vegetation indices have been developed, with their effectiveness depending on the context and the extent of vegetation cover. Proper timing of fieldwork and measurement is essential for accurate results. The paper presents a comprehensive review of the remote sensing and GIS based methods of soil salinity estimation including salinity indices, vegetation indices, regression methos, neural network methods plus, sensing approaches, and satellite data utilized in soil salinity mapping. The majority of recent studies favour remote sensing technology over traditional methods due to its cost-effectiveness and efficiency. The choice of mapping approach is context-dependent, and there is no universally superior method. This review underscores the critical role of remote sensing in addressing the challenges posed by soil salinity, offering a promising avenue for monitoring and managing this imperative global concern.

Integrating Multi-Point Geostatistics, Machine Learning, and Image Correlation for Characterizing Positional Errors in Remote-Sensing Images of High Spatial Resolution

Remote-sensing images of high spatial resolution (HSR) are valuable sources of fine-grained spatial information for various applications, such as urban surveys and governance. There is continuing research on positional errors in remote-sensing images and their impacts in geoprocessing and applications. This paper explores the combined use of multi-point geostatistics (MPS), machine learning—in particular, generalized additive modeling (GAM)—and computer-image correlation for characterizing positional errors in images—in particular, HSR images. These methods are employed because of the merits of MPS in being flexible for non-parametric and joint simulation of positional errors in X and Y coordinates, the merits of GAM in being capable of handling non-stationarity in-positional errors through error de-trending, and the merits of computer-image correlation in being cost-effective in furnishing the training data (TD) required in MPS. Procedurally, image correlation is applied to identify homologous image points in reference-test image pairs to extract image displacements automatically in constructing TD. To cope with the complexity of urban scenes and the unavailability of truly orthorectified images, visual screening is performed to clean the raw displacement data to create quality-enhanced TD, while manual digitization is used to obtain reference sample data, including conditioning data (CD), for MPS and test data for performance evaluation. GAM is used to decompose CD and TD into trends and residuals. With CD and TD both de-trended, the direct sampling (DS) algorithm for MPS is applied to simulate residuals over a simulation grid (SG) at 80 m spatial resolution. With the realizations of residuals and, hence, positional errors generated in this way, the means, standard deviation, and cross correlation in bivariate positional errors at SG nodes are computed. The simulated error fields are also used to generate equal-probable realizations of vertices that define some road centerlines (RCLs), selected for this research through interpolation over the aforementioned simulated error fields, leading to error metrics for the RCLs and for the lengths of some RCL segments. The enhanced georectification of the RCLs is facilitated through error correction. A case study based in Shanghai municipality, China, was carried out, using HSR images as part of generalized point clouds that were developed. The experiment results confirmed that by using the proposed methods, spatially explicit positional-error metrics, including means, standard deviation, and cross correlation, can be quantified flexibly, with those in the selected RCLs and the lengths of some RCL segments derived easily through error propagation. The reference positions of these RCLs were obtained through error correction. The positional accuracy gains achieved by the proposed methods were found to be comparable with those achieved by conventional image georectification, in which the CD were used as image-georectification control data. The proposed methods are valuable not only for uncertainty-informed image geolocation and analysis, but also for integrated geoinformation processing.

Fuzzy C‐means clustering algorithm based on superpixel merging and multi‐feature adaptive fusion measurement

AbstractThe fuzzy C‐means clustering (FCM) algorithm is widely used in greyscale and colour image segmentation, especially in real colour images. However, in the process of interested regions extraction, it performs barely satisfactory due to the use of single distance metric in traditional FCM. In order to address the issue, a fuzzy C‐means clustering algorithm based on superpixel merging and multi‐feature adaptive fusion measurement (FCM‐SM) are proposed. First of all, since superpixel image adapts well to irregular image boundaries, the FCM algorithm is elevated to the superpixel level. Moreover, the superpixel merging module based on colour similarity is introduced to improve the traditional watershed transformation by incorporating more gradient and intensity information. Finally, the multi‐feature adaptive fusion measurement is designed in the clustering process to incorporate multiple sources of spatial information, which comprehensively measures the information from different fields to enhance the segmentation capability further. Experiments performed on Berkeley Segmentation Dataset and Brain Tumor Segmentation Dataset demonstrate that the proposed algorithm provides better segmentation results than benchmarks during the object extraction.

Three-dimensional Softmax Mechanism Guided Bidirectional GRU Networks for Hyperspectral Remote Sensing Image Classification

Hyperspectral data is a valuable source of both spectral and spatial information. However, to enhance the classification accuracy of hyperspectral image features, it is crucial to capture the spatial spectral features of image elements. The recent years have witnessed the potentials of deep learning methods have shown great promise in the hyperspectral image classification due to their ability to model complex structures and extract multiple features in an end-to-end fashion. Since hyperspectral images can be viewed as sequential data, we propose a novel three-dimensional Softmax mechanism-guided bidirectional GRU network (TDS-BiGRU) for HSI classification. By utilizing a bidirectional GRU to process the sequence data, our method can significantly reduce the processing time. Furthermore, the proposed three-dimensional Softmax mechanism leverages three branches to capture cross-latitude interactions and calculate Softmax weights, which enables us to obtain deeper features with greater discriminative power. The experimental results demonstrate that the proposed method outperforms several prevalent algorithms on four hyperspectral remote sensing datasets. Additionally, we conduct thorough comparisons and ablation tests, which further confirm the effectiveness of our approach.

Search and assessment of urban heat islands on digital satellite images

Formulation of the problem. The development of urbanization processes resulted in Urban Heat Islands (UHI) - ur-ban areas of high temperatures due to the dominance of artificial surfaces that absorb and retain heat. UHIs adversely affect the environmen-tal conditions of cities and people's health. Satellite images in the thermal infrared range of the electromagnetic spectrum allow identifying areas of the high thermal back-ground of urbanized territories for management decisions support aimed at improving urban landscapes. The aim of the work is to develop a methodology for temperature anomalies analy-sis and urban heat islands detection on digital space images from Landsat 8 satellite in the thermal infrared range of the electromagnetic spectrum. Methodology of UHI detection. The methodology proposed in the paper includes a sequence of stages of collecting, and preprocessing satellite data, generating land sur-face temperature maps, identifying temperature anomalies, comprehensive analysis of high-temperature zones based on different-time data, and identifying areas of high temperatures stable over time. Land Surface Temperature estimation. Land Surface Temperature (LST) is esti-mated from the band 10 data of space images from the Landsat 8 satellite. The initial data are subjected to atmospheric correction, after which the spectral radiation in the band is converted to brightness temperature, and then to LST (Celsius degrees). UHI mapping. On each survey date, UHIs are identified as temperature anomalies - LST map pixels with values higher than the LST mean plus standard deviation within the study area. Time series analysis of satellite images. UHI mapping is based on a time series of Landsat 8 images acquired during the warm season (when ambient temperatures are above zero) to eliminate the effect of snow and ice on the observation results. According to the time series of LST maps of different years, stable temperature anomalies are dis-tinguished - UHIs presented on all images of the time series. Conclusions. Time-stable UHIs of the urbanized territory are mapped and ana-lyzed using Landsat 8 LST data time series. The areas of the high thermal background are compared with the classes of the earth's surface. It is concluded that increasing ur-ban density contributes to the accumulation of heat and increases the ambient tem-perature. The maps of LST distribution, vegetation indices, and UHI generated in the work can be used in urban planning tasks together with traditional sources of spatial information to obtain new information on improving the city's resistance to heat waves. UHI observation data are necessary for zoning areas with different land use types, for example, increasing the amount of open space and greenery, and improving the thermal comfort of citizens.

Predicting Multidimensional Poverty with Machine Learning Algorithms: An Open Data Source Approach Using Spatial Data

This paper presents a methodology to estimate the multidimensional poverty index using spatial data at the street block level. The data used in this study were obtained from Open Street Maps and ESA’s land use cover, which are freely available sources of spatial information. The study employs five machine-learning algorithms, including Catboost, Lightboost, and Random Forest, to estimate the multidimensional poverty index with spatial granularity. The results indicate that these models achieve promising performance in predicting poverty levels in Medellín, Colombia. The results showed that the Random Forest algorithm achieved the highest performance, with an MAE of 0.07504. Furthermore, the spatial distribution of the multidimensional poverty estimate was highly correlated with the true values of the distribution. This work contributes to predicting multidimensional poverty by demonstrating the potential of machine learning algorithms to utilize accessible spatial data. By providing evidence of the feasibility of estimating poverty levels at a granular spatial level, this methodology offers a powerful tool for policymakers to make poverty social interventions with low-cost evidence. Furthermore, this study has important implications for poverty eradication efforts in developing countries, where access to reliable data remains challenging.

BatMap – authoritative distribution maps for Australian bats

BatMap is a project that has enabled the development of spatial distribution maps for all mainland Australian and Tasmanian bat species. The distribution maps were developed by bat experts and are actively updated and maintained by utilising the collective knowledge of the members of the Australasian Bat Society. Distribution maps are the most accurate maps that are currently available for Australian bat species. The distribution maps are freely available on-line in a variety of formats. The distribution maps have been used for a variety of purposes, including the assessment of the spatial and taxonomic accuracy of bat records from ecological datasets, particularly datasets that are managed by the Atlas of Living Australia, which are considered by many to be the key source of spatial information for bats. BatMap enables better conservation outcomes for Australian bat fauna through the provision of authoritative, readily accessible and usable maps that can be used to increase the accuracy and reliability of bat records that contribute to decision making processes for conservation purposes.

Machine Learning Models for Prediction of Soil Properties in the Riparian Forests

Spatial variability of soil properties is a critical factor for the planning, management, and exploitation of soil resources. Thus, the use of different digital soil mapping models to provide accuracy plays a crucial role in providing soil physicochemical properties maps. Soil spatial variability in forest stands is not well-known in Iran. Meanwhile, riparian buffers are important for several services such as providing high water quality, nutrient recycling, and buffering agricultural production. Accordingly, in this research, 103 soil samples were taken using the Latin hypercubic method in the Maroon riparian forest of Behbahan and agricultural lands in the vicinity of the forest to evaluate the spatial variability of soil nitrogen, potassium, organic carbon, C:N ratio, pH, calcium carbonate, sand, silt, clay, and bulk density. Different machine learning models, including artificial neural networks, random forest, cubist regression tree, and k-nearest neighbor were used to compare the estimation of soil properties. Moreover, three main sources of spatial information including remote sensing images, digital elevation model, and climate parameters were used as ancillary data. Our results indicated that the random forest model has the best results in estimating soil pH, nitrogen, potassium, and bulk density. In contrast, the cubist regression tree indicated the best estimation for organic carbon, C:N ratio, phosphorous, and clay. Further, artificial neural networks showed the best estimation for calcium carbonate, sand, and silt contents. Our results revealed that geospatial information such as terrain parameters, climate parameters, and satellite images could be well used as ancillary data for the spatial mapping of soil physiochemical properties in riparian forests and agricultural lands. In conclusion, a specific machine learning model needs to be used for each soil property to provide highly accurate maps with less error.

A robust and adaptive spatial-spectral fusion model for PlanetScope and Sentinel-2 imagery

ABSTRACT Satellite sensors usually compromise between their spatial and spectral resolutions due to the limitations of data volume and signal-to-noise ratio, such as the Very High spatial Resolution (VHR) PlanetScope (PS, four or five 3-m bands) and medium spatial resolution (med-resolution) Sentinel-2 (S2, ten 10-m or 20-m bands) constellations. Concomitant with the growing demand for satellite images with ample spatial details and spectral signatures, Spatial-Spectral image Fusion (SSF) is important for blending the spatial resolution of PS and the spectral resolution of S2 to produce synthetic 3-m image products in the ten bands for different applications. However, the existing studies conducted for fusing PS and S2 data present limited spatial and spectral fidelities to original PS and S2 bands. Hence, this study presents a new SSF method named Robust and Adaptive Spatial-Spectral image Fusion Model (RASSFM) for that purpose. RASSFM improves the spatial and spectral fidelities of the results through: (1) combining the spectral mapping and the spectral correlation to obtain the high-quality spatial information sources for the S2 bands, (2) utilizing the neighborhood information considering both spatial and spectral constraints to improve the spectral fidelities of the fused bands. We conducted the SSF tests at the degraded and original spatial resolutions to comprehensively evaluate the proposed RASSFM method. Furthermore, we examined the performance of RASSFM in four study sites with highly mixed regular or desert urban, heterogeneous agricultural, and vegetation-dominated landscapes. Moreover, we compared our method with twenty representative SSF methods developed for similar purposes. The results demonstrate that RASSFM not only outperforms the other methods but has robust performance in different landscapes and comparable accuracies in different bands, thereby advancing the fusion of VHR and med-resolution images from PS and S2, respectively.

Use of Auditory Cues and Other Strategies as Sources of Spatial Information for People with Visual Impairment When Navigating Unfamiliar Environments.

This paper explores strategies that the visually impaired use to obtain information in unfamiliar environments. This paper also aims to determine how natural sounds that often exist in the environment or the auditory cues that are installed in various facilities as a source of guidance are prioritized and selected in different countries. The aim was to evaluate the utilization of natural sounds and auditory cues by users who are visually impaired during mobility. The data were collected by interviewing 60 individuals with visual impairments who offered their insights on the ways they use auditory cues. The data revealed a clear contrast in methods used to obtain information at unfamiliar locations and in the desire for the installation of auditory cues in different locations between those who use trains and those who use different transportation systems. The participants demonstrated a consensus on the need for devices that provide on-demand minimal auditory feedback. The paper discusses the suggestions offered by the interviewees and details their hopes for adjusted auditory cues. The study argues that auditory cues have high potential for improving the quality of life of people who are visually impaired by increasing their mobility range and independence level. Additionally, this study emphasizes the importance of a standardized design for auditory cues, which is a change desired by the interviewees. Standardization is expected to boost the efficiency of auditory cues in providing accurate information and assistance to individuals with visual impairment regardless of their geographical location. Regarding implications for practitioners, the study presents the need to design systems that provide minimal audio feedback to reduce the masking of natural sounds. The design of new auditory cues should utilize the already-existing imagination skills that people who have a visual impairment possess. For example, the pitch of the sound should change to indicate the direction of escalators and elevators and to distinguish the location of male and female toilets.

A direct lateral entorhinal cortex to hippocampal CA2 circuit conveys social information required for social memory

The hippocampus is essential for different forms of declarative memory, including social memory, the ability to recognize and remember a conspecific. Although recent studies identify the importance of the dorsal CA2 region of the hippocampus in social memory storage, little is known about its sources of social information. Because CA2, like other hippocampal regions, receives its major source of spatial and non-spatial information from the medial and lateral subdivisions of entorhinal cortex (MEC and LEC), respectively, we investigated the importance of these inputs for social memory. Whereas MEC inputs to CA2 are dispensable, the direct inputs to CA2 from LEC are both selectively activated during social exploration and required for social memory. This selective behavioral role of LEC is reflected in the stronger excitatory drive it provides to CA2 compared with MEC. Thus, a direct LEC → CA2 circuit is tuned to convey social information that is critical for social memory.

Chains of Spatial and Temporal Precipitation Occurrence Predictability Across the Continental U.S.

Both spatial and temporal information sources contribute to the predictability of precipitation occurrence at a given location. These sources, and the level of predictability they provide, are relevant to forecasting and understanding precipitation processes at different time scales. We use information theory-based measures to construct connected “chains of influence” of spatial extents and timescales of precipitation occurrence predictability across the continental U.S, based on gridded daily precipitation data. These regions can also be thought of as “footprints” or regions where precipitation states tend to be most synchronized. We compute these chains of precipitation influence for grid cells in the continental US, and study metrics regarding their lengths, extents, and curvature for different seasons. We find distinct geographic and seasonal patterns, particularly longer chain lengths during the summer that are indicative of larger spatial extents for storms. While synchronous, or instantaneous, relationships are strongest for grid cells in the same region, lagged relationships arise as chains reach areas farther from the original cell. While this study focuses on precipitation occurrence predictability given only information about precipitation, it could be extended to study spatial and temporal properties of other driving factors.

Exposure of the Canadian wildland–human interface and population to wildland fire, under current and future climate conditions

In Canada, recent fire seasons have demonstrated the threat of wildland fire in the wildland–human interface (WHI) areas, where forest fuels intermingle with or abut housing, industry, and infrastructure. Although fire activity is expected to increase further in the coming decades as a result of climate change, no WHI-specific estimates of wildland fire exposure are currently available. This study combines spatial and demographic information sources to estimate the current and future wildland fire exposures, as reflected by fire return intervals (FRI) of WHI areas and populations across Canada. The WHI covers 17.3% of the forested area in Canada. Within the WHI, we found that 19.4% of the area currently experiences FRI of ≤250 years, but by the end of the century, this could increase to 28.8% under Representative Concentration Pathway (RCP) 2.6 and to 43.3% under RCP 8.5. Approximately 12.3% of the Canadian population currently live in the wildland–urban interface (WUI), which includes 32.1% of the on-reserve First Nations population. Currently, 17.8% of the on-reserve WUI population is exposed to FRI of ≤250 years, compared with only 4.7% of the remaining WUI population. By 2100, these proportions could reach 39.3% and 17.4%, respectively, under the less optimistic climatic scenarios (RCP 8.5).

Thalamus and claustrum control parallel layer 1 circuits in retrosplenial cortex.

The granular retrosplenial cortex (RSG) is critical for both spatial and non-spatial behaviors, but the underlying neural codes remain poorly understood. Here, we use optogenetic circuit mapping in mice to reveal a double dissociation that allows parallel circuits in superficial RSG to process disparate inputs. The anterior thalamus and dorsal subiculum, sources of spatial information, strongly and selectively recruit small low-rheobase (LR) pyramidal cells in RSG. In contrast, neighboring regular-spiking (RS) cells are preferentially controlled by claustral and anterior cingulate inputs, sources of mostly non-spatial information. Precise sublaminar axonal and dendritic arborization within RSG layer 1, in particular, permits this parallel processing. Observed thalamocortical synaptic dynamics enable computational models of LR neurons to compute the speed of head rotation, despite receiving head direction inputs that do not explicitly encode speed. Thus, parallel input streams identify a distinct principal neuronal subtype ideally positioned to support spatial orientation computations in the RSG.

Small‐scale spatial distributions of long‐finned pilot whales change over time, but foraging hot spots are consistent: Significance for marine wildlife tourism management

AbstractData collected opportunistically aboard marine wildlife tourism vessels are an inexpensive source of spatial information on the target species. Although these data are often challenging to analyze, they can be used to monitor spatiotemporal changes in species distribution and behavior. Disruptions from whale‐watching vessels to behaviors such as foraging can be particularly harmful to cetaceans, but impacts could be reduced if areas essential for these sensitive behaviors are identified. We used data collected onboard whale‐watching vessels to explore space‐use patterns in long‐finned pilot whales (Globicephala melas) off northern Cape Breton Island, Canada, an area where tourism is essential. Encounters with pilot whales between 2011–2016 occurred twice as far offshore than during 2003–2006 and 2008, and foraging activity decreased. Despite the changes in distribution and activity budgets, we identified two hot spots of foraging activity that persisted through time. These identified foraging hot spots comprised only a small proportion (20 km2) of the range used by whale‐watching vessels. Adaptive local management (e.g., voluntary codes of conduct) focused on limiting interactions in these energetically important areas may help reduce any potential impacts from whale‐watching and promote the continued viability of the whale population and the tourism industry that relies on it.

ApeX-Vigne: experiences in monitoring vine water status from within-field to regional scales using crowdsourcing data from a free mobile phone application

Monitoring vine water status is a major issue for vineyard management because water constraints impact both the quality and the quantity of the harvest. Existing methods are often costly and complex to implement. ApeX-Vigne is a free mobile application developed to facilitate the collection and geolocation of 50 vine apex observations to characterise vine shoot growth and classify it into 3 growth categories. The application also provides the user with a simple estimate of vine water status based on shoot growth. This paper presents the results obtained over two seasons (2019 and 2020) after the launch of the Apex-Vigne application and its use over a large wine producing region in the south of France. An existing method was adapted for evaluating the interest of the application based on the number of installations and uninstallations. The results showed that the application had more than 1200 downloads and 6000 observations made in the 2020 season. Examples from the commercially collected data showed that ApeX-Vigne can be used as a tool for characterizing water stress at within-field and inter-field scales. Finally, it was also demonstrated that by enabling the massive and centralized collection of spatial field and within-field scale observations of shoot growth, the ApeX-Vigne data was able to characterise the spatial structure of vine water status at the regional scale. Access to this new source of information offers opportunities for the management of water resources at a regional scale as well as for site- and vineyard-specific management. These results also raised new research questions on the joint use of this new source of spatial data with other sources of high spatial resolution information.

The use of index images for decoding the vegetation cover of inner mountain Dagestan, Russia

Aim. Selection and analysis of index images suitable for deciphering the vegetation cover in the conditions of inner mountain Dagestan.Methods. The study was carried out on the basis of multi-temporal satellite images of high spatial resolution, obtained by the imaging system of the Sentinel-2 series satellite, using methods of digital processing of geoimages. Processing was carried out using the capabilities of the Google Earth Engine geoservice.Results. Multi-temporal index images were obtained for the territory of inner mountain Dagestan. The time series of seasonal changes in the indices (NDVI, SAVI, EVI) were analyzed, making it possible to reveal the phenological patterns of vegetation and to map the vegetation cover on this basis. Schemes for decoding vegetation have been created by which areas are distinguished according to the following characteristics: devoid of vegetation, herbaceous vegetation of varying degrees of density or woody (deciduous and coniferous).Conclusion. When studying vegetation cover using index images in a range of natural conditions, it is necessary to take into account the natural features of the territory, as well using additional sources of spatial information including field research methods.