Integration of Landsat–Sentinel Time Series and Flowering Phenology for Mapping Planted Forests and Distinguishing Tree Crops.
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| Title: | Integration of Landsat–Sentinel Time Series and Flowering Phenology for Mapping Planted Forests and Distinguishing Tree Crops. |
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| Authors: | Zhao, Xuan1 (AUTHOR) zhaoxuan@gdut.edu.cn, Tan, Qian1 (AUTHOR), Cai, Yanpeng1 (AUTHOR) |
| Source: | Remote Sensing. Jun2026, Vol. 18 Issue 11, p1825. 24p. |
| Subjects: | Tree crops, Forest mapping, Afforestation, Biomass production, Remote sensing, Flowering time, Landsat satellites |
| Abstract: | Highlights: What are the main findings? Existing datasets may confuse tree crops and planted forests. Combining biomass accumulation rates and flowering (floration) spectral signatures, and integrating Landsat with Sentinel time series, enables accurate discrimination of planted trees versus tree crops and captures flowering phenology useful for mapping. What are the implications of the main findings? Current estimates of planted forest extent and related carbon/land use statistics may be substantially overestimated, requiring revision for policy, carbon accounting, and restoration monitoring. Using biomass growth rates, floration spectra, and fused Landsat–Sentinel data improves mapping accuracy and should be adopted to better target conservation, restoration, and agricultural/forestry planning. Planted forests are increasingly promoted to meet rising demand for forest products and restore degraded lands, but their extent and ecological implications are often misrepresented because tree crops (e.g., orchards, plantation agriculture) exhibit similar spectral and spatial signatures to planted forests. This study aims to improve differentiation between planted forests and tree crops within national-scale restoration programs. We combined Landsat-derived NDVI time series targeting disturbance-related phenological windows with the LandTrendr algorithm to map planting/clearcutting events and fused in situ spectral measurements with Sentinel-2 to develop a modified orchard flowering index (MOFI). Random forest models evaluated classification performance using combinations of spatiotemporal spectral features, biomass accumulation proxies, and the MOFI. Incorporating the MOFI improved discrimination of tree crops versus planted forests, raising the planted forest F1 from 0.751 to 0.843. The combination of the MOFI and spatiotemporal spectral features achieved the highest accuracy (F1 = 0.843). The results show tree crops are concentrated on plains and gentle mountain slopes, while plantations occur mostly on slopes > 15°, with tree crops comprising 27.1% of mapped planted tree area. These findings imply that many national planted forest map estimates may be biased without phenology- and biomass-informed methods and that integrating Landsat and Sentinel phenology metrics supports more accurate monitoring for management and policy. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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| Abstract: | Highlights: What are the main findings? Existing datasets may confuse tree crops and planted forests. Combining biomass accumulation rates and flowering (floration) spectral signatures, and integrating Landsat with Sentinel time series, enables accurate discrimination of planted trees versus tree crops and captures flowering phenology useful for mapping. What are the implications of the main findings? Current estimates of planted forest extent and related carbon/land use statistics may be substantially overestimated, requiring revision for policy, carbon accounting, and restoration monitoring. Using biomass growth rates, floration spectra, and fused Landsat–Sentinel data improves mapping accuracy and should be adopted to better target conservation, restoration, and agricultural/forestry planning. Planted forests are increasingly promoted to meet rising demand for forest products and restore degraded lands, but their extent and ecological implications are often misrepresented because tree crops (e.g., orchards, plantation agriculture) exhibit similar spectral and spatial signatures to planted forests. This study aims to improve differentiation between planted forests and tree crops within national-scale restoration programs. We combined Landsat-derived NDVI time series targeting disturbance-related phenological windows with the LandTrendr algorithm to map planting/clearcutting events and fused in situ spectral measurements with Sentinel-2 to develop a modified orchard flowering index (MOFI). Random forest models evaluated classification performance using combinations of spatiotemporal spectral features, biomass accumulation proxies, and the MOFI. Incorporating the MOFI improved discrimination of tree crops versus planted forests, raising the planted forest F1 from 0.751 to 0.843. The combination of the MOFI and spatiotemporal spectral features achieved the highest accuracy (F1 = 0.843). The results show tree crops are concentrated on plains and gentle mountain slopes, while plantations occur mostly on slopes > 15°, with tree crops comprising 27.1% of mapped planted tree area. These findings imply that many national planted forest map estimates may be biased without phenology- and biomass-informed methods and that integrating Landsat and Sentinel phenology metrics supports more accurate monitoring for management and policy. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 20724292 |
| DOI: | 10.3390/rs18111825 |
