File Download

There are no files associated with this item.

  Links for fulltext
     (May Require Subscription)
Supplementary

Article: Scale matters: Spatial resolution impacts tropical leaf phenology characterized by multi-source satellite remote sensing with an ecological-constrained deep learning model

TitleScale matters: Spatial resolution impacts tropical leaf phenology characterized by multi-source satellite remote sensing with an ecological-constrained deep learning model
Authors
KeywordsDeep learning
Ecosystem deciduousness
Leaf phenology
Phenological diversity
Satellite remote sensing
Spatial resolution
Spectral unmixing
Tropical forest
Issue Date6-Feb-2024
PublisherElsevier
Citation
Remote Sensing of Environment, 2024, v. 304 How to Cite?
AbstractAccurate monitoring of tropical leaf phenology, such as the leaf-on/off status, at both individual and ecosystem scales is essential for understanding and modelling tropical forest carbon and water cycles, and their sensitivity to climate change. The discrepancy between tree-crown size and pixel size (i.e., spatial resolution) across orbital sensors can affect the capability of cross-scale phenology monitoring, an aspect that remains understudied. To examine the impact of spatial resolution on tropical leaf phenology monitoring, we applied a spectral index-guided, ecologically constrained autoencoder (IG-ECAE) to automatically generate a deciduousness metric (i.e., percentage of upper canopy area that is leaf-off status within an image pixel) from simulated VIS-NIR PlanetScope data at a range of resolutions from 3 m to 30 m, as well as from VIS-NIR data of three satellite platforms with the same range of spatial resolutions (3 m PlanetScope, 10 m Sentinel-2, and 30 m Landsat-8). We compared the deciduousness metrics derived from the simulated and satellite data to corresponding measurements derived from WorldView-2 (three sites) and local phenocams (four sites) at five tropical forest sites. Our results revealed that: (1) the IG-ECAE model captured the amount of deciduousness across spatial scales, with the highest accuracy obtained from PlanetScope, followed by Sentinel-2 and Landsat-8; (2) coarser spatial resolutions led to lower accuracies in tropical deciduousness monitoring, as demonstrated by both simulated PlanetScope data across various spatial resolutions and real satellite data; and (3) while not as accurate in capturing fine-scale tropical phenological diversity as PlanetScope, Sentinel-2 provided satisfactory monitoring of deciduousness seasonality at the ecosystem level consistently across all phenocam sites, whereas Landsat-8 failed to do so. Collectively, this study provides a robust assessment for advancing cross-scale tropical leaf phenology monitoring with potential for extension to pan-tropical regions and highlights the impact of spatial resolution on such monitoring efforts.
Persistent Identifierhttp://hdl.handle.net/10722/344671
ISSN
2023 Impact Factor: 11.1
2023 SCImago Journal Rankings: 4.310

 

DC FieldValueLanguage
dc.contributor.authorSong, G-
dc.contributor.authorWang, J-
dc.contributor.authorZhao, Y-
dc.contributor.authorYang, D-
dc.contributor.authorLee, CKF-
dc.contributor.authorGuo, Z-
dc.contributor.authorDetto, M-
dc.contributor.authorAlberton, B-
dc.contributor.authorMorellato, P-
dc.contributor.authorNelson, B-
dc.contributor.authorWu, J-
dc.date.accessioned2024-07-31T06:22:55Z-
dc.date.available2024-07-31T06:22:55Z-
dc.date.issued2024-02-06-
dc.identifier.citationRemote Sensing of Environment, 2024, v. 304-
dc.identifier.issn0034-4257-
dc.identifier.urihttp://hdl.handle.net/10722/344671-
dc.description.abstractAccurate monitoring of tropical leaf phenology, such as the leaf-on/off status, at both individual and ecosystem scales is essential for understanding and modelling tropical forest carbon and water cycles, and their sensitivity to climate change. The discrepancy between tree-crown size and pixel size (i.e., spatial resolution) across orbital sensors can affect the capability of cross-scale phenology monitoring, an aspect that remains understudied. To examine the impact of spatial resolution on tropical leaf phenology monitoring, we applied a spectral index-guided, ecologically constrained autoencoder (IG-ECAE) to automatically generate a deciduousness metric (i.e., percentage of upper canopy area that is leaf-off status within an image pixel) from simulated VIS-NIR PlanetScope data at a range of resolutions from 3 m to 30 m, as well as from VIS-NIR data of three satellite platforms with the same range of spatial resolutions (3 m PlanetScope, 10 m Sentinel-2, and 30 m Landsat-8). We compared the deciduousness metrics derived from the simulated and satellite data to corresponding measurements derived from WorldView-2 (three sites) and local phenocams (four sites) at five tropical forest sites. Our results revealed that: (1) the IG-ECAE model captured the amount of deciduousness across spatial scales, with the highest accuracy obtained from PlanetScope, followed by Sentinel-2 and Landsat-8; (2) coarser spatial resolutions led to lower accuracies in tropical deciduousness monitoring, as demonstrated by both simulated PlanetScope data across various spatial resolutions and real satellite data; and (3) while not as accurate in capturing fine-scale tropical phenological diversity as PlanetScope, Sentinel-2 provided satisfactory monitoring of deciduousness seasonality at the ecosystem level consistently across all phenocam sites, whereas Landsat-8 failed to do so. Collectively, this study provides a robust assessment for advancing cross-scale tropical leaf phenology monitoring with potential for extension to pan-tropical regions and highlights the impact of spatial resolution on such monitoring efforts.-
dc.languageeng-
dc.publisherElsevier-
dc.relation.ispartofRemote Sensing of Environment-
dc.subjectDeep learning-
dc.subjectEcosystem deciduousness-
dc.subjectLeaf phenology-
dc.subjectPhenological diversity-
dc.subjectSatellite remote sensing-
dc.subjectSpatial resolution-
dc.subjectSpectral unmixing-
dc.subjectTropical forest-
dc.titleScale matters: Spatial resolution impacts tropical leaf phenology characterized by multi-source satellite remote sensing with an ecological-constrained deep learning model-
dc.typeArticle-
dc.identifier.doi10.1016/j.rse.2024.114027-
dc.identifier.scopuseid_2-s2.0-85184149192-
dc.identifier.volume304-
dc.identifier.issnl0034-4257-

Export via OAI-PMH Interface in XML Formats


OR


Export to Other Non-XML Formats