Current status of Landsat program, science, and applications

Wulder, Michael A.; Loveland, Thomas R.; Roy, David P.; Crawford, Christopher; Masek, Jeffrey G.; Woodcock, Curtis E.; Allen, Richard G.; Anderson, Martha C.; Belward, Alan; Cohen, Warren B.; Dwyer, John L.; Erb, Angela; Gao, Feng; Griffiths, Patrick; Helder, Dennis L.; Hermosilla, Txomin; Hipple, James D.; Hostert, Patrick; Hughes, Michael J.; Huntington, Justin L.; Johnson, David M.; Kennedy, Robert E.; Kiliç, Ayşe; Li, Zhan; Lymburner, Leo; McCorkel, Joel; Pahlevan, Nima; Scambos, T. A.; Schaaf, Crystal B.; Schott, John R.; Sheng, Yongwei; Storey, James C.; Vermote, Éric; Vogelmann, James E.; White, Joanne C.; Wynne, Randolph H.; Zhu, Zhe

doi:10.1016/j.rse.2019.02.015

Cited by 714 publications

(363 citation statements)

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“…A key advantage of Landsat in this regard lies in the global availability of 30 m‐resolution imagery since the 1980s (at lower spatial resolutions even since the 1970s; Wulder et al. ). This offers great flexibility in terms of generating consistent predictors for habitat modeling across space and time and matching predictor data with available species records.…”

Section: Discussionmentioning

confidence: 99%

“…Overall, our study highlights the considerable potential of spectral-temporal metrics from medium-resolution satellite imagery for monitoring, managing and conserving wildlife. A key advantage of Landsat in this regard lies in the global availability of 30 m-resolution imagery since the 1980s (at lower spatial resolutions even since the 1970s; Wulder et al 2019). This offers great flexibility in terms of generating consistent predictors for habitat modeling across space and time and matching predictor data with available species records.…”

Section: Discussionmentioning

confidence: 99%

“…Moreover, the Landsat archive provides freely available, global imagery back to the 1970s (Wulder et al. , ), which should represent major opportunities for assessing wildlife habitat in space and time (Nagendra et al. ; He et al.…”

Section: Introductionmentioning

confidence: 99%

“…; Wulder et al. ) and rapidly improving computational capacities (e.g. through cloud computing platforms; Gorelick et al.…”

Section: Introductionmentioning

confidence: 99%

“…Recently, the provision of Landsat data in analysisready formats (Dwyer et al 2018;Wulder et al 2019) and rapidly improving computational capacities (e.g. through cloud computing platforms; Gorelick et al 2017), have dramatically improved the possibilities for analyzing large sets of Landsat images.…”

Section: Introductionmentioning

confidence: 99%

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Habitat metrics based on multi‐temporal Landsat imagery for mapping large mammal habitat

Oeser

Heurich

Senf

et al. 2019

Remote Sens Ecol Conserv

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Up‐to‐date and fine‐scale habitat information is essential for managing and conserving wildlife. Studies assessing wildlife habitat commonly rely on categorical land‐cover maps as predictors in habitat models. However, broad land‐cover categories often do not adequately capture key habitat features and generating robust land‐cover maps is challenging and laborious. Continuous variables derived directly from satellite imagery provide an alternative for capturing land‐cover characteristics in habitat models. Improved data availability and processing capacities now allow integrating all available images from medium‐resolution sensors in compositing approaches that derive spectral‐temporal metrics at the pixel level, summarizing spectral responses over time. In this study, we assessed the usefulness of such metrics derived from Landsat imagery for mapping wildlife habitat. We categorize spectral‐temporal metrics into habitat metrics characterizing different aspects of wildlife habitat. Comparing the performance of these metrics against categorical land‐cover maps in habitat models for lynx, red deer and roe deer, we found that models using habitat metrics consistently outperformed models based on categorical land‐cover maps, with average improvements of 13.7% in model AUC and 9.7% in the Continuous Boyce Index. Performance increases were larger for seasonal habitat models, indicating that the habitat metrics capture intra‐annual variability in habitat conditions better than land‐cover maps. Comparing suitability maps to ancillary data further revealed that our habitat metrics were sensitive to fine‐scale heterogeneity in habitat associated with forest structure. Overall, our study highlights the considerable potential of Landsat‐based spectral temporal metrics for assessing wildlife habitat. Given these metrics can be derived directly and in an automatized fashion from globally and freely available Landsat imagery, they open up new possibilities for monitoring habitat dynamics in space and time.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%