High-Resolution Satellite Imagery Is an Important yet Underutilized Resource in Conservation Biology

Abstract

Technological advances and increasing availability of high-resolution satellite imagery offer the potential for more accurate land cover classifications and pattern analyses, which could greatly improve the detection and quantification of land cover change for conservation. Such remotely-sensed products, however, are often expensive and difficult to acquire, which prohibits or reduces their use. We tested whether imagery of high spatial resolution (≤5 m) differs from lower-resolution imagery (≥30 m) in performance and extent of use for conservation applications. To assess performance, we classified land cover in a heterogeneous region of Interior Atlantic Forest in Paraguay, which has undergone recent and dramatic human-induced habitat loss and fragmentation. We used 4 m multispectral IKONOS and 30 m multispectral Landsat imagery and determined the extent to which resolution influenced the delineation of land cover classes and patch-level metrics. Higher-resolution imagery more accurately delineated cover classes, identified smaller patches, retained patch shape, and detected narrower, linear patches. To assess extent of use, we surveyed three conservation journals (Biological Conservation, Biotropica, Conservation Biology) and found limited application of high-resolution imagery in research, with only 26.8% of land cover studies analyzing satellite imagery, and of these studies only 10.4% used imagery ≤5 m resolution. Our results suggest that high-resolution imagery is warranted yet under-utilized in conservation research, but is needed to adequately monitor and evaluate forest loss and conversion, and to delineate potentially important stepping-stone fragments that may serve as corridors in a human-modified landscape. Greater access to low-cost, multiband, high-resolution satellite imagery would therefore greatly facilitate conservation management and decision-making.

Functional connectivity and home range inferred at a microgeographic landscape genetics scale in a desert‐dwelling rodent

“…These three variables were measured at each trap location and at the center of the trapping web (i.e., 21 points per trapping web and 105 by transect), for a total of 315 points; the values for the entire study area were obtained by the krigging interpolation method (Holdaway, 1996) Using lower-resolution imagery to characterize land cover can lead to incorrect or misleading evaluations of connectivity if not verified on the field (Zeller, Nijhawan, Salom-Pérez, Potosme, & Hines, 2011), and on the other hand, some satellite bands are not available in Google Earth imagery, thus lacking information about certain landscape features (Boyle et al, 2014). 1 km 2 resolution; Hijmans, Cameron, Parra, Jones, & Jarvis, 2005), we generated a set of environmental surfaces at our study spatial scale which were hypothesized to affect survival or movement of D. merriami: humidity (as a proxy for precipitation at a very local scale), temperature, elevation, plant cover (hereafter vegetation), and soil.…”

The Uncertainty of Plot-Scale Forest Height Estimates from Complementary Spaceborne Observations in the Taiga-Tundra Ecotone

“…Observations of current vegetation may be improved with archival or legacy data from different sensors acquired at different times, to derive vegetation structure characteristics. These applications and measurements could be useful for a broad spectrum of spatial scientists [51] if the uncertainty associated with the measurements is well-documented.…”

Coupled social and ecological outcomes of agricultural intensification in Costa Rica and the future of biodiversity conservation in tropical agricultural regions

“…These fine-scale habitat features cannot be identified using lower-resolution (30 m Landsat) imagery; the availability of high-resolution (5 m RapidEye) maps provides a new opportunity to assess the contributions of these fine-scale features to forest connectivity and to determine which land cover types are most likely to retain these features (Boyle et al, 2014). To quantify the fine-scale landscape features in each land cover type in the SJLS region, we used a tree cover map based on 5 m RapidEye and the zonal statistics tool in ArcMap 10.1.…”