An empirical evaluation of camera trap study design : How many, how long and when?
Kays, Roland ; Arbogast, Brian S. ; Baker-Whatton, Megan ; Beirne, Chris ; Boone, Hailey M. ; Bowler, Mark ; Burneo, Santiago F. ; Cove, Michael V. ; Ding, Ping ; Espinosa, Santiago ; Gonçalves, André Luis Sousa ; Hansen, Christopher P. ; Jansen, Patrick A. ; Kolowski, Joseph M. ; Knowles, Travis W. ; Lima, Marcela Guimarães Moreira ; Millspaugh, Joshua ; McShea, William J. ; Pacifici, Krishna ; Parsons, Arielle W. ; Pease, Brent S. ; Rovero, Francesco ; Santos, Fernanda ; Schuttler, Stephanie G. ; Sheil, Douglas ; Si, Xingfeng ; Snider, Matt ; Spironello, Wilson R. - \ 2020
Methods in Ecology and Evolution 11 (2020)6. - ISSN 2041-210X - p. 700 - 713.
camera traps - community ecology - detectability - mammals - relative abundance - species richness - study design - wildlife surveys
Camera traps deployed in grids or stratified random designs are a well-established survey tool for wildlife but there has been little evaluation of study design parameters. We used an empirical subsampling approach involving 2,225 camera deployments run at 41 study areas around the world to evaluate three aspects of camera trap study design (number of sites, duration and season of sampling) and their influence on the estimation of three ecological metrics (species richness, occupancy and detection rate) for mammals. We found that 25–35 camera sites were needed for precise estimates of species richness, depending on scale of the study. The precision of species-level estimates of occupancy (ψ) was highly sensitive to occupancy level, with <20 camera sites needed for precise estimates of common (ψ > 0.75) species, but more than 150 camera sites likely needed for rare (ψ < 0.25) species. Species detection rates were more difficult to estimate precisely at the grid level due to spatial heterogeneity, presumably driven by unaccounted habitat variability factors within the study area. Running a camera at a site for 2 weeks was most efficient for detecting new species, but 3–4 weeks were needed for precise estimates of local detection rate, with no gains in precision observed after 1 month. Metrics for all mammal communities were sensitive to seasonality, with 37%–50% of the species at the sites we examined fluctuating significantly in their occupancy or detection rates over the year. This effect was more pronounced in temperate sites, where seasonally sensitive species varied in relative abundance by an average factor of 4–5, and some species were completely absent in one season due to hibernation or migration. We recommend the following guidelines to efficiently obtain precise estimates of species richness, occupancy and detection rates with camera trap arrays: run each camera for 3–5 weeks across 40–60 sites per array. We recommend comparisons of detection rates be model based and include local covariates to help account for small-scale variation. Furthermore, comparisons across study areas or times must account for seasonality, which could have strong impacts on mammal communities in both tropical and temperate sites.
Defining and applying the concept of Favourable Reference Values for species habitats under the EU Birds and Habitats Directives : examples of setting favourable reference values
Bijlsma, R.J. ; Agrillo, E. ; Attorre, F. ; Boitani, L. ; Brunner, A. ; Evans, P. ; Foppen, R. ; Gubbay, S. ; Janssen, J.A.M. ; Kleunen, A. van; Langhout, W. ; Pacifici, M. ; Ramirez, I. ; Rondinini, C. ; Roomen, M. van; Siepel, H. ; Swaaij, C.A.M. van; Winter, H.V. - \ 2019
Wageningen : Wageningen Environmental Research (Wageningen Environmental Research report 2929) - 219
Defining and applying the concept of Favourable Reference Values for species habitats under the EU Birds and Habitats Directives : technical report
Bijlsma, R.J. ; Agrillo, E. ; Attorre, F. ; Boitani, L. ; Brunner, A. ; Evans, P. ; Foppen, R. ; Gubbay, S. ; Janssen, J.A.M. ; Kleunen, A. van; Langhout, W. ; Noordhuis, R. ; Pacifici, M. ; Ramirez, I. ; Rondinini, C. ; Roomen, M. van; Siepel, H. ; Winter, H.V. - \ 2019
Wageningen : Wageningen Environmental Research (Wageningen Environmental Research report 2928) - 93
Understanding angular effects in VHR imagery and their significance for urban land-cover model portability : A study of two multi-angle in-track image sequences
Matasci, Giona ; Longbotham, Nathan ; Pacifici, Fabio ; Kanevski, Mikhail ; Tuia, Devis - \ 2015
ISPRS Journal of Photogrammetry and Remote Sensing 107 (2015). - ISSN 0924-2716 - p. 99 - 111.
Atmospheric compensation - Domain adaptation - Histogram matching - Image classification - Maximum Mean Discrepancy - Multi-angle acquisitions
This paper investigates the angular effects causing spectral distortions in multi-angle remote sensing imagery. We study two WorldView-2 multispectral in-track sequences acquired over the cities of Atlanta, USA, and Rio de Janeiro, Brazil, consisting of 13 and 20 co-located images, respectively. The sequences possess off-nadir acquisition angles up to 47.5° and bear markedly different sun-satellite configurations with respect to each other. Both scenes comprise classic urban structures such as buildings of different size, road networks, and parks. First, we quantify the degree of distortion affecting the sequences by means of a non-linear measure of distance between probability distributions, the Maximum Mean Discrepancy. Second, we assess the ability of a classification model trained on an image acquired at a certain view angle to predict the land-cover of all the other images in the sequence. The portability across the sequence is investigated for supervised classifiers of different nature by analyzing the evolution of the classification accuracy with respect to the off-nadir look angle. For both datasets, the effectiveness of physically- and statistically-based normalization methods in obtaining angle-invariant data spaces is compared and synergies are discussed. The empirical results indicate that, after a suitable normalization (histogram matching, atmospheric compensation), the loss in classification accuracy when using a model trained on the near-nadir image to classify the most off-nadir acquisitions can be reduced to as little as 0.06 (Atlanta) or 0.03 (Rio de Janeiro) Kappa points when using a SVM classifier.
SVM active learning approach for image classification using spatial information
Pasolli, Edoardo ; Melgani, Farid ; Tuia, Devis ; Pacifici, Fabio ; Emery, William J. - \ 2014
IEEE Transactions on Geoscience and Remote Sensing 52 (2014)4. - ISSN 0196-2892 - p. 2217 - 2223.
Active learning - Image classification - Spatial information - Support vector machine (SVM) - Very high resolution (VHR)
In the last few years, active learning has been gaining growing interest in the remote sensing community in optimizing the process of training sample collection for supervised image classification. Current strategies formulate the active learning problem in the spectral domain only. However, remote sensing images are intrinsically defined both in the spectral and spatial domains. In this paper, we explore this fact by proposing a new active learning approach for support vector machine classification. In particular, we suggest combining spectral and spatial information directly in the iterative process of sample selection. For this purpose, three criteria are proposed to favor the selection of samples distant from the samples already composing the current training set. In the first strategy, the Euclidean distances in the spatial domain from the training samples are explicitly computed, whereas the second one is based on the Parzen window method in the spatial domain. Finally, the last criterion involves the concept of spatial entropy. Experiments on two very high resolution images show the effectiveness of regularization in spatial domain for active learning purposes.
Statistical assessment of dataset shift and model portability in multi-angle in-track image acquisitions
Matasci, Giona ; Longbotham, Nathan ; Pacifici, Fabio ; Kanevski, Mikhail ; Tuia, Devis - \ 2013
In: 2013 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2013 - Proceedings. - - p. 4134 - 4137.
Domain adaptation - Image classification - Maximum mean discrepancy - Multi-angle acquisitions
In this study we propose an evaluation of the angular effects altering the spectral response of the land-cover over multi-angle remote sensing image acquisitions. The shift in the statistical distribution of the pixels observed in an in-track sequence of WorldView-2 images is analyzed by means of a kernel-based measure of distance between probability distributions. Afterwards, the portability of supervised classifiers across the sequence is investigated by looking at the evolution of the classification accuracy with respect to the changing observation angle. In this context, the efficiency of various physically and statistically based preprocessing methods in obtaining angle-invariant data spaces is compared and possible synergies are discussed.
Spatial coordination between stem cell activity and cell differentiation in the root meristem
Moubayidin, L. ; Mambro, R. Di; Sozzani, R. ; Pacifici, E. ; Salvi, E. ; Terpstra, I. ; Bao, D. ; Dijken, A. van; Dello loio, R. ; Perilli, S. ; Ljung, K. ; Benfey, P.N. ; Heidstra, R. ; Costantino, P. ; Sabatini, S. - \ 2013
Developmental Cell 26 (2013)4. - ISSN 1534-5807 - p. 405 - 415.
gras gene family - arabidopsis root - auxin biosynthesis - scarecrow - expression - thaliana - transport - division - growth - niche
A critical issue in development is the coordination of the activity of stem cell niches with differentiation of their progeny to ensure coherent organ growth. In the plant root, these processes take place at opposite ends of the meristem and must be coordinated with each other at a distance. Here, we show that in Arabidopsis, the gene SCR presides over this spatial coordination. In the organizing center of the root stem cell niche, SCR directly represses the expression of the cytokinin-response transcription factor ARR1, which promotes cell differentiation, controlling auxin production via the ASB1 gene and sustaining stem cell activity. This allows SCR to regulate, via auxin, the level of ARR1 expression in the transition zone where the stem cell progeny leaves the meristem, thus controlling the rate of differentiation. In this way, SCR simultaneously controls stem cell division and differentiation, ensuring coherent root growth.
Improving active learning methods using spatial information
Pasolli, Edoardo ; Melgani, Farid ; Tuia, Devis ; Pacifici, Fabio ; Emery, William J. - \ 2011
In: 2011 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2011 - Proceedings. - - p. 3923 - 3926.
Active learning - spatial information - support vector machines (SVMs) - very-high-resolution (VHR) images
Active learning process represents an interesting solution to the problem of training sample collection for the classification of remote sensing images. In this work, we propose a criterion based on the spatial information that can be used in combination with a spectral criterion in order to improve the selection of training samples. Experimental results obtained on a very high resolution image show the effectiveness of regularization in spatial domain and open challenging perspectives for terrain campaigns planning.
Spectral-textural endmember extraction
Zortea, Maciel ; Tuia, Devis ; Pacifici, Fabio ; Plaza, Antonio - \ 2010
In: 2nd Workshop on Hyperspectral Image and Signal Processing. -
Endmember extraction - Hyperspectral imaging - Spectral unmixing - Texture features
Several available techniques for endmember extraction and spectral unmixing use only the spectral information contained in the hyperspectral data. In this paper, we introduce a novel method for spatial-spectral endmember extraction which incorporates texture features in the quantification of spatial information (jointly with spectral information). Experimental results with simulated and real hyperspectral data sets indicate that textural information could assist the extraction of spectral endmembers, although a challenging issue still remains: how to combine the final set of endmember candidates (obtained by merging the individual sets of candidates found using spectral, textural and joint spectral - textural information) in order to provide a relevant final solution.