# Before and After: A Multiscale Remote Sensing Assessment of the Sinop Dam, Mato Grosso, Brazil
## 1 Introduction  
Remote sensing has become a fundamental source of information for conservation and environmental science [1] supporting long term monitoring and mapping of natural and anthropogenic changes, such as expansion of the agricultural frontier (e.g., [2, 3]), deforestation (e.g., [4, 5, 6]), mining (e.g., [7, 8, 9]), and hydroelectric dams construction (e.g., [10, 11]). Early moderate resolution (30-60 m) imagery revealed the preponderance of forest degradation in proximity to clearcutting throughout the Brazilian Amazon [12], an alarming trend that continues today [13, 14, 15]. Due to the availability of long-term archives (e.g., ~50 years from Landsat satellites), many studies rely on moderate (i.e., 30 m) or coarse (i.e., MODIS, 250-500 m) resolution imagery. The spatial resolution of these images does not allow for precise delineation of the forest/non forest boundary nor the shoreline of water bodies with narrow channels (e.g., narrower than the satellite image spatial resolution) [5, 16] (Figure 1). Also, frequent cloud cover in the tropics [17] and the long revisit period of many optical satellites can preclude the acquisition of imagery at critical moments for dynamic phenomena (e.g., filling of hydropower reservoirs). The recent increased availability of high revisitfrequency and high spatial resolution satellite imagery (e.g., 3-5 m) from multi-satellite constellations such as RapidEye and PlanetScope have allowed for monitoring events in near real-time [18] and dense multi-temporal assessments (e.g., [19; 20]). Rapid advances in the development of Unmanned Aerial Systems (UAS) allow for highly flexible methods to monitor and analyze ecosystems at much finer spatial and temporal resolutions than is feasible from satellites or manned aircraft [21; 22; 23] (Figure 1). The different platforms, from consumer to professional grades (e.g., [24]), and a variety of payloads, from digital cameras to specialized sensors (e.g., [23; 25; 26]), allow for customized configurations. In particular, reconstruction of the landscape through Structure-from-Motion (SfM) with multi-view stereo (MVS) photogrammetry has become popular in many fields [25; 26]. In addition to numerous terrestrial applications, the 3D point clouds and orthomosaics generated from SfM-MVS with high level of detail (e.g., <1 cm) have also been shown to effectively assess aquatic ecosystem characteristics such as freshwater habitat complexity, ichthyclological habitat classes, bathymetry, fluvial and aquatic topography, hydraulics, and geomorphology [27; 28; 29]. Worldwide, rivers are experiencing an increase in hydroelectric dam development, with thousands of major dams and small hydropower plants already in operation, under construction, or planned for the coming decades [30; 31; 32]. In many cases, pre-construction estimates of land cover to be flooded are inaccurate [33]. Their impact on the aquatic fauna through river connectivity fragmentation, interruption of migrations, and habitat modification is also not well studied prior to construction [34]. The combined effects of the dams constructed in the Amazon represent a major threat to fish biodiversity [35; 36]. The modification of the annual flood pulse due to hydropower impoundment also has a local effect on endemic species [36], and the permanently flooded areas in the reservoir become colonized by native opportunistic and invasive fish species, bringing considerable  
Figure 1: Illustration of the different levels of spatial detail that can be resolved by satellite and UAS platforms. The moderate resolution systems include common satellite-based sensors such as the CBERS (China–Brazil Earth Resources Satellite), Landsat and Sentinel-2 group of systems. The high spatial resolution systems include common satellite-based sensors such as RapidEye, Worldview and Planet Dove constellations and systems. The UAS represented refers to low-altitude systems (<100 m AGL). change to the local fish assemblage [37; 38].