Toward large-scale mapping of tree crops with high-resolution satellite imagery and deep learning algorithms: A case study of olive orchards in Morocco

Chenxi Lin, Zhenong Jin, David Mulla, Rahul Ghosh, Kaiyu Guan, Vipin Kumar, Yaping Cai

Research output: Contribution to journalArticlepeer-review

Abstract

Timely and accurate monitoring of tree crop extent and productivities are necessary for informing policy-making and investments. However, except for a very few tree species (e.g., oil palms) with obvious canopy and extensive planting, most small-crown tree crops are understudied in the remote sensing domain. To conduct large-scale small-crown tree mapping, several key questions remain to be answered, such as the choice of satellite imagery with different spatial and temporal resolution and model generalizability. In this study, we use olive trees in Morocco as an example to explore the two abovementioned questions in mapping small-crown orchard trees using 0.5 m DigitalGlobe (DG) and 3 m Planet imagery and deep learning (DL) techniques. Results show that compared to DG imagery whose mean overall accuracy (OA) can reach 0.94 and 0.92 in two climatic regions, Planet imagery has limited capacity to detect olive orchards even with multi-temporal information. The temporal information of Planet only helps when enough spatial features can be captured, e.g., when olives are with large crown sizes (e.g., >3 m) and small tree spacings (e.g., <3 m). Regarding model generalizability, experiments with DG imagery show a decrease in F1 score up to 5% and OA to 4% when transferring models to new regions with distribution shift in the feature space. Findings from this study can serve as a practical reference for many other similar mapping tasks (e.g., nuts and citrus) around the world.

Original languageEnglish (US)
Article number1740
JournalRemote Sensing
Volume13
Issue number9
DOIs
StatePublished - 2021

Bibliographical note

Funding Information:
Funding: This research was funded by the National Aeronautics and Space Administration (NASA) Land Cover Land Use Change program, grant number 80NSSC20K1485.

Publisher Copyright:
© 2021 by the authors. Licensee MDPI, Basel, Switzerland.

Keywords

  • Deep learning
  • High-resolution imagery
  • Land cover
  • Remote sensing
  • Tree crop

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