Spatiotemporal Variation in Avian Migration Phenology: Citizen Science Reveals Effects of Climate Change

A growing number of studies have documented shifts in avian migratory phenology in response to climate change, and yet there is a large amount of unexplained variation in the magnitude of those responses across species and geographic regions. We use a database of citizen science bird observations to explore spatiotemporal variation in mean arrival dates across an unprecedented geographic extent for 18 common species in North America over the past decade, relating arrival dates to mean minimum spring temperature. Across all species and geographic locations, species shifted arrival dates 0.8 days earlier for every °C of warming of spring temperature, but it was common for some species in some locations to shift as much as 3–6 days earlier per °C. Species that advanced arrival dates the earliest in response to warming were those that migrate more slowly, short distance migrants, and species with broader climatic niches. These three variables explained 63% of the interspecific variation in phenological response. We also identify a latitudinal gradient in the average strength of phenological response, with species shifting arrival earlier at southern latitudes than northern latitudes for the same degree of warming. This observation is consistent with the idea that species must be more phenologically sensitive in less seasonal environments to maintain the same degree of precision in phenological timing

Scaled Euclidean 3D Reconstruction Based On Externally Uncalibrated Cameras

Previous work shows that based on five noncoplanar correspondences of two uncalibrated cameras, 3D reconstruction can be achieved under projective models, or based on four non-coplanar correspondences of two uncalibrated cameras, 3D reconstruction can be achieved under affine models, with three unknown parameters. In this paper, we show that based on four coplanar correspondences of two externally uncalibrated cameras, 3D reconstruction can be achieved in Euclidean space with only one unknown scaling parameter. Moreover, the unknown scale factor is the physical distance from the camera center to the plane formed by the four points in 3D space. If this distance is known a priori, then the 3D structure can be completely recovered. Both simulated and real data experimental results show that our reconstruction algorithm works reasonably robustly. 1 Introduction 3D reconstruction is an important problem and still remains a difficult problem. Over the years, this problem has been related t..

3D Reconstruction Based on Homography Mapping

Previous work shows that based on the fundamental matrix from two views, 3D structures can be recovered up to an unknown projectivity. In this paper, we show that based on four coplanar correspondences of two externally uncalibrated cameras, 3D reconstruction can be achieved in Euclidean space with only one uniform scale factor and up to two real solutions. It is shown that this scale factor is the physical distance from the camera center to the plane formed by the four points in 3D space. Consequently, if this distance is known a priori, then the 3D structure can be completely determined. In order to disambiguate the two solutions, a third view is required in general to give a unique solution. In practice, since the real data are always corrupted with noise, more coplanar correspondences are used and a least squares solution is applied to obtain the estimation of the homography matrix. Experimental results on both simulated and real data show that this reconstruction algorithm works r..

Obstacle Detection Based on Partial 3D Reconstruction

Three different algorithms for qualitative obstacle detection are presented in this paper. Each one is based on different assumptions. The first two algorithms are aimed at yes/no obstacle detection without indicating which points are obstacles. They have the advantage of fast determination of the existence of obstacles in a scene based on the solvability of a linear system. The first algorithm uses information about the ground plane, while the second algorithm only assumes that the ground is planar. The third algorithm continuously estimates the ground plane, and based on that determines the height of each matched point in the scene. Experimental results are presented for real and simulated data, and performances of the three algorithms under different noise levels are compared in simulation. We conclude that in terms of the robustness of performance, the third one works best. 1 Introduction Obstacle detection is an important issue in mobile robotics. One of the questions that is ex..

Approximate Image Mappings Between Nearly Boresight Aligned Optical and Range Sensors

This technical report summarizes the intrinsic sensor parameters for the range, IR and color sensor used in the Fort Carson data collection and explores sensor-to-sensor image mapping under di erent assumptions regarding relative sensor placement. The default case is to assume perfectly boresight aligned placement, and then the implications of di erent deviations from this perfect placement are considered. Included in this report is a description of the calibration process used to recover the color sensor parameters. Akey result shown in detail is the relative equivalence of planar sensor translation and small angle pan and tilt for points of known depth. This simplifying approximation ha