Quantitative Remote Sensing for Vegetation Phenology and Regional Landscape Patterns
A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing in Agriculture and Vegetation".
Deadline for manuscript submissions: 30 January 2025 | Viewed by 1037
Special Issue Editors
Interests: climate change; vegetation dynamic; remote sensing; deep learning
Interests: remote sensing phenology; climate change; UAV lidar application
Interests: carbon cycle and global change; ecological model; vegetation phenology
Special Issue Information
Dear Colleagues,
Vegetation phenology refers to specific life cycle events of plants, such as budbreak, flowering, leafout/greenup, fructification, and senescence. It is a crucial factor in assessing the impact of climate change on vegetation and influences terrestrial energy exchange and global carbon cycling. The rapid advancement in remote sensing technologies has broadened the scope of phenological studies by providing high-resolution, long-term, and diverse datasets that enable the precise monitoring and analysis of vegetation changes. These technologies facilitate a comprehensive understanding of land surface phenology across different scales and various ecosystems, thereby enhancing insights into ecological processes and the impacts of environmental changes. Over the past decades, extensive research has been conducted on the dynamics of vegetation phenology, its drivers and mechanisms, and its ecological implications and climatic feedback. This Special Issue is dedicated to studies that explore the latest advancements in the detection, understanding, and application of vegetation phenology based on various remote sensing techniques and datasets at landscape and regional scales. We invite researchers to submit papers on all aspects of vegetation phenology, including variation in landscape heterogeneity, cross-resolution comparison, relationships with carbon or hydrological cycling; and response to climate change. Related topics may include, but are not limited to, the following:
- Innovative extraction algorithms for vegetation phenology, such as land surface phenology or leaf phenology, estimated from reflectance-based indices or solar-induced chlorophyll fluorescence observations.
- Comparative studies of vegetation phenology across different scales using various datasets, including UAV imagery, Sentinel, Planetscope, Landsat, and MODIS.
- Analysis of temporal variations and spatial patterns in remote sensing phenology and their determining factors at landscape or regional scales, including tropical forests, dryland woodlands, coastal forests, boreal forests, and commercial plantations.
- Practical applications in agriculture, such as mapping or classification based on phenology stages using deep learning, carrying capacity assessment, and yield estimation.
Dr. Ke Huang
Dr. Jiaxing Zu
Dr. Ning Chen
Dr. Xiaowei Tong
Guest Editors
Manuscript Submission Information
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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
Keywords
- remote sensing
- vegetation dynamic
- climate change
- landscape pattern
- cross-scale
- vegetation phenology applications
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