Using Multi-Resolution Satellite Data to Quantify Land Dynamics: Applications of PlanetScope Imagery for Cropland and Tree-Cover Loss Area Estimation

dc.contributor.authorPickering, Jeffrey
dc.contributor.authorTyukavina, Alexandra
dc.contributor.authorKhan, Ahmad
dc.contributor.authorPotapov, Peter
dc.contributor.authorAdusei, Bernard
dc.contributor.authorHansen, Matthew C.
dc.contributor.authorLima, André
dc.date.accessioned2023-11-02T18:35:45Z
dc.date.available2023-11-02T18:35:45Z
dc.date.issued2021-06-04
dc.description.abstractThe Planet constellation of satellites represents a significant advance in the availability of high cadence, high spatial resolution imagery. When coupled with a targeted sampling strategy, these advances enhance land-cover and land-use monitoring capabilities. Here we present example regional and national-scale area-estimation methods as a demonstration of the integrated and efficient use of mapping and sampling using public medium-resolution (Landsat) and commercial high resolution (PlanetScope) imagery. Our proposed method is agnostic to the geographic region and type of land cover and change, which is demonstrated by applying the method across two very different geographies and thematic classes. Wheat extent is estimated in Punjab, Pakistan, for the 2018/2019 growing season, and tree-cover loss area is estimated over Peru for 2017 and 2018. We used a time series of PlanetScope imagery to classify a sample of 5 × 5 km blocks for each region and produce area estimates of 55,947 km2 (±9.0%) of wheat in Punjab and 5398 km2 (±9.1%) of tree-cover loss in Peru. We also demonstrate the use of regression estimation utilizing population information from Landsat-based maps to reduce standard errors of the sample-based estimates. Resulting regression estimates have SEs of 3.6% and 5.1% for Pakistan and Peru, respectively. The combination of daily global coverage and high spatial resolution of Planet imagery improves our ability to monitor crop phenology and capture ephemeral tree-cover loss and degradation dynamics, while Landsat-based maps provide wall-to-wall information to target the sample and increase precision of the estimates through the use of regression estimation.
dc.description.urihttps://doi.org/10.3390/rs13112191
dc.identifierhttps://doi.org/10.13016/dspace/w21j-qb5b
dc.identifier.citationPickering, J.; Tyukavina, A.; Khan, A.; Potapov, P.; Adusei, B.; Hansen, M.C.; Lima, A. Using Multi-Resolution Satellite Data to Quantify Land Dynamics: Applications of PlanetScope Imagery for Cropland and Tree-Cover Loss Area Estimation. Remote Sens. 2021, 13, 2191.
dc.identifier.urihttp://hdl.handle.net/1903/31252
dc.language.isoen_US
dc.publisherMDPI
dc.relation.isAvailableAtCollege of Behavioral & Social Sciencesen_us
dc.relation.isAvailableAtGeographyen_us
dc.relation.isAvailableAtDigital Repository at the University of Marylanden_us
dc.relation.isAvailableAtUniversity of Maryland (College Park, MD)en_us
dc.subjectPlanetScope imagery
dc.subjectPakistan
dc.subjectwheat
dc.subjectPeru
dc.subjecttree-cover loss
dc.subjectdegradation
dc.subjectland-cover
dc.subjectsampling
dc.titleUsing Multi-Resolution Satellite Data to Quantify Land Dynamics: Applications of PlanetScope Imagery for Cropland and Tree-Cover Loss Area Estimation
dc.typeArticle
local.equitableAccessSubmissionNo

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