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Article

Toward Large-Scale Mapping of Tree Crops with High-Resolution Satellite Imagery and Deep Learning Algorithms: A Case Study of Olive Orchards in Morocco

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Department of Bioproducts and Biosystems Engineering, University of Minnesota Twin Cities, St Paul, MN 55108, USA
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Department of Soil, Water, and Climate, University of Minnesota Twin Cities, St Paul, MN 55108, USA
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Department of Computer Science and Engineering, University of Minnesota Twin Cities, Minneapolis, MN 55455, USA
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College of Agricultural, Consumer and Environmental Sciences, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
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National Center for Supercomputing Applications, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
*
Author to whom correspondence should be addressed.
Academic Editor: Naoto Yokoya
Remote Sens. 2021, 13(9), 1740; https://doi.org/10.3390/rs13091740
Received: 22 March 2021 / Revised: 21 April 2021 / Accepted: 28 April 2021 / Published: 30 April 2021
(This article belongs to the Special Issue Remote Sensing for Crop Mapping)
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. View Full-Text
Keywords: remote sensing; deep learning; land cover; tree crop; high-resolution imagery remote sensing; deep learning; land cover; tree crop; high-resolution imagery
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MDPI and ACS Style

Lin, C.; Jin, Z.; Mulla, D.; Ghosh, R.; Guan, K.; Kumar, V.; Cai, Y. Toward Large-Scale Mapping of Tree Crops with High-Resolution Satellite Imagery and Deep Learning Algorithms: A Case Study of Olive Orchards in Morocco. Remote Sens. 2021, 13, 1740. https://doi.org/10.3390/rs13091740

AMA Style

Lin C, Jin Z, Mulla D, Ghosh R, Guan K, Kumar V, Cai Y. Toward Large-Scale Mapping of Tree Crops with High-Resolution Satellite Imagery and Deep Learning Algorithms: A Case Study of Olive Orchards in Morocco. Remote Sensing. 2021; 13(9):1740. https://doi.org/10.3390/rs13091740

Chicago/Turabian Style

Lin, Chenxi, Zhenong Jin, David Mulla, Rahul Ghosh, Kaiyu Guan, Vipin Kumar, and Yaping Cai. 2021. "Toward Large-Scale Mapping of Tree Crops with High-Resolution Satellite Imagery and Deep Learning Algorithms: A Case Study of Olive Orchards in Morocco" Remote Sensing 13, no. 9: 1740. https://doi.org/10.3390/rs13091740

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