# Sensitivity of Optical Satellites to Estimate Windthrow Tree-Mortality in a Central Amazon Forest
## 1 Introduction  
Windthrows (i.e., trees snapped and uprooted by wind) are the major mechanism of tree mortality in the Amazon, and can influence forest structure, species composition, and carbon balance [1, 2, 3, 4]. Windthrows are associated with extreme rainfall events, mostly produced by mesoscale convective systems [5]. Under climate change, the intensity and frequency of convective storms is predicted to increase [6, 7]. Thus, accurately quantifyingthe occurrence of windthrows and associated tree mortality can contribute to understanding their ecosystem effects, and to predict how forests will respond to altered disturbance regimes [8; 9; 10; 11]. Optical satellite imagery with medium spatial resolution (from 10 m to 39.9 m pixel size) [12], such as Landsat, combined with field data has allowed researchers to assess patterns of tree damage and mortality, and to monitor forest recovery over decades of subsequent succession in different regions [13; 14; 15]. These studies demonstrated that windthrows affect a large portion of the Amazon and occur with a range of intensities and sizes. The Central and Northwestern Amazon regions concentrate the highest incidence of large-scale (>30 ha) events [13; 16; 17]. Landsat offers broad coverage of the Earth's surface and long-term imagery data [18; 19; 20], allowing for reliable estimates of windthrow tree mortality at the stand, landscape, and regional scales [13; 14; 17]. However, robust estimates of tree mortality depend on the compatibility between the spatial resolution of optical sensors and the grain size of targets [21; 22]. For this reason, using Landsat to detect windthrows formed by clusters of less than \(\sim\)6-8 dead trees is challenging [23]. Even for larger windthrows (>5 ha), Landsat assessments may underestimate tree mortality by not accurately capturing relatively lower levels of disturbance on the periphery of impacted patches or due to surviving trees and the relatively fast regrowth of the natural regeneration [13; 24]. Obtaining reliable estimates of tree mortality using remote sensing is not a trivial task. The quality of such estimates is highly dependent on the target of interest (e.g., single or clustering dead-trees, [25; 26]), the methods used to quantify windthrow severity [27], and the characteristics of the employed sensors (e.g., spatial, temporal, and spectral resolutions [21; 28; 29]). The spatial resolution of satellites has been treated as an important variable in this process as it can improve the detection capability of small and diffuse vegetation damage, and thus minimize the influence of spectral mixing [30]. Spectral mixing tends to dilute the spectral signature of tree damage and mortality with other elements within the pixels. This includes shadow, exposed soil, or surviving trees within a matrix of impacted forest [27; 31]. High spatial resolution satellite data, such as QuickBird, IKONOS, GeoEye, and WorldView, are suitable for detecting small canopy gaps in dense tropical forests [32; 33; 34; 35; 36] and have been shown to be efficient, even for detecting individual tree mortality in the Amazon [25; 26; 34; 37; 38]. In addition to optical satellites, other approaches such as SAR (Synthetic-Aperture Radar [39; 40]), LiDAR [41; 42; 43] and aerial photos [44] have been tested to estimate tree-mortality associated with windthrows. SAR offers data that are less dependent on weather effects and with spatial resolutions comparable to high- and medium-resolution satellites [45; 46]. However, this technology has not yet been tested for the Amazon, and a study conducted in a temperate forest showed that windthrow detection was not reliable due to shading and overlap effects that are common in mountainous areas, as well as the polarization and distortions associated with the angle of incidence during image acquisition [46].