An ecologically-constrained deep learning model for tropical leaf phenology monitoring using PlanetScope satellites

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Wang, Jing ; Song, Guangqin ; Liddell, Michael ; Morellato, Patricia ; Lee, Calvin K. F. ; Yang, Dedi ; Alberton, Bruna ; Detto, Matteo ; Ma, Xuanlong ; Zhao, Yingyi ; Yeung, Henry C. H. ; Zhang, Hongsheng ; Ng, Michael ; Nelson, Bruce W. ; Huete, Alfredo ; Wu, Jin

• Tipo de documento: Artigo Científico
• Fonte: REMOTE SENSING OF ENVIRONMENT; v. 286, p. 16-pg., 2023-01-06.

Resumo

In tropical forests, leaf phenology signals leaf-on/off status and exhibits considerable variability across scales from a single tree-crown to the entire forest ecosystem. Such phenology signals importantly regulate large-scale biogeochemical cycles and regional climate. PlanetScope CubeSats data with a 3-m resolution and near-daily global coverage provide an unprecedented opportunity to monitor both fine-and ecosystem-scale phenology variability along large environmental gradients. However, a scalable method that accurately characterizes leaf phenology from PlanetScope with biophysically meaningful metrics remains lacking. We developed an index -guided, ecologically constrained autoencoder (IG-ECAE) method to automatically derive a deciduousness metric (percentage of upper tree canopies with leaf-off status within an image pixel) from PlanetScope. The IG-ECAE first estimated the reflectance spectra of leafy/leafless canopies based on their spectral indices charac-teristics, then used the derived reflectance spectra to guide an autoencoder deep learning method with additional ecological constraints to refine the reflectance spectra, and finally used linear spectral unmixing to estimate the relative abundance of leafless canopies (or deciduousness) per PlanetScope image pixel. We tested the IG-ECAE method at 16 tropical forest sites spanning multiple continents and a large precipitation gradient (1470-2819 mm year -1). Among these sites, we evaluated the PlanetScope-derived deciduousness against corresponding measures derived from WorldView-2 (n = 9 sites) and local phenocams (n = 9 sites). Our results show that PlanetScope-derived deciduousness agrees: 1) with that derived from WorldView-2 at the patch level (90 m x 90 m) with r2 = 0.89 across all sites; and 2) with that derived from phenocams to quantify ecosystem-scale sea-sonality with r2 ranging from 0.62 to 0.96. These results demonstrate the effectiveness and scalability of IG-ECAE in characterizing the wide variability in deciduousness across scales from pixels to forest ecosystems, and from a single date to the full annual cycle, indicating the potential for using high-resolution satellites to track the large-scale phenological patterns and response of tropical forests to climate change. (AU)

• Processo FAPESP: 14/00215-0 - Fenologia remota e os padrões de trocas foliares ao longo de um gradiente de sazonalidade
• Beneficiário: Bruna de Costa Alberton
• Modalidade de apoio: Bolsas no Brasil - Doutorado
• Processo FAPESP: 13/50155-0 - Combining new technologies to monitor phenology from leaves to ecosystems
• Beneficiário: Leonor Patricia Cerdeira Morellato
• Modalidade de apoio: Auxílio à Pesquisa - Programa de Pesquisa sobre Mudanças Climáticas Globais - PITE
• Processo FAPESP: 19/16191-6 - Acoplando fenocâmeras e satélites para monitorar mudanças em ambientes sazonalmente secos no Hemisfério Sul
• Beneficiário: Leonor Patricia Cerdeira Morellato
• Modalidade de apoio: Auxílio à Pesquisa - Regular
• Processo FAPESP: 16/01413-5 - Utilizando a fenologia vegetativa para investigar os controladores de fluxo de energia dos ecossistemas em vegetações tropicais
• Beneficiário: Bruna de Costa Alberton
• Modalidade de apoio: Bolsas no Exterior - Estágio de Pesquisa - Doutorado
• Processo FAPESP: 19/11835-2 - Fenologia de florestas secas decíduas: uma comparação em múltiplas escalas utilizando fenocâmeras e drones
• Beneficiário: Leonor Patricia Cerdeira Morellato
• Modalidade de apoio: Auxílio à Pesquisa - Regular