More Like this

1. Mapping Temperate Forest Phenology Using Tower, UAV, and Ground-Based Sensors

   https://doi.org/10.3390/drones4030056  

   Atkins, Jeff W. ; Stovall, Atticus E. ; Yang, Xi ( September 2020 , Drones)

   null (Ed.)

   Phenology is a distinct marker of the impacts of climate change on ecosystems. Accordingly, monitoring the spatiotemporal patterns of vegetation phenology is important to understand the changing Earth system. A wide range of sensors have been used to monitor vegetation phenology, including digital cameras with different viewing geometries mounted on various types of platforms. Sensor perspective, view-angle, and resolution can potentially impact estimates of phenology. We compared three different methods of remotely sensing vegetation phenology—an unoccupied aerial vehicle (UAV)-based, downward-facing RGB camera, a below-canopy, upward-facing hemispherical camera with blue (B), green (G), and near-infrared (NIR) bands, and a tower-based RGB PhenoCam, positioned at an oblique angle to the canopy—to estimate spring phenological transition towards canopy closure in a mixed-species temperate forest in central Virginia, USA. Our study had two objectives: (1) to compare the above- and below-canopy inference of canopy greenness (using green chromatic coordinate and normalized difference vegetation index) and canopy structural attributes (leaf area and gap fraction) by matching below-canopy hemispherical photos with high spatial resolution (0.03 m) UAV imagery, to find the appropriate spatial coverage and resolution for comparison; (2) to compare how UAV, ground-based, and tower-based imagery performed in estimating the timing of the spring phenological transition. We found that a spatial buffer of 20 m radius for UAV imagery is most closely comparable to below-canopy imagery in this system. Sensors and platforms agree within +/− 5 days of when canopy greenness stabilizes from the spring phenophase into the growing season. We show that pairing UAV imagery with tower-based observation platforms and plot-based observations for phenological studies (e.g., long-term monitoring, existing research networks, and permanent plots) has the potential to scale plot-based forest structural measures via UAV imagery, constrain uncertainty estimates around phenophases, and more robustly assess site heterogeneity. 

   more » « less
2. Using Near-Infrared-Enabled Digital Repeat Photography to Track Structural and Physiological Phenology in Mediterranean Tree–Grass Ecosystems

   https://doi.org/10.3390/rs10081293  

   Luo, Yunpeng ; El-Madany, Tarek S. ; Filippa, Gianluca ; Ma, Xuanlong ; Ahrens, Bernhard ; Carrara, Arnaud ; Gonzalez-Cascon, Rosario ; Cremonese, Edoardo ; Galvagno, Marta ; Hammer, Tiana W. ; et al ( August 2018 , Remote Sensing)

   Tree–grass ecosystems are widely distributed. However, their phenology has not yet been fully characterized. The technique of repeated digital photographs for plant phenology monitoring (hereafter referred as PhenoCam) provide opportunities for long-term monitoring of plant phenology, and extracting phenological transition dates (PTDs, e.g., start of the growing season). Here, we aim to evaluate the utility of near-infrared-enabled PhenoCam for monitoring the phenology of structure (i.e., greenness) and physiology (i.e., gross primary productivity—GPP) at four tree–grass Mediterranean sites. We computed four vegetation indexes (VIs) from PhenoCams: (1) green chromatic coordinates (GCC), (2) normalized difference vegetation index (CamNDVI), (3) near-infrared reflectance of vegetation index (CamNIRv), and (4) ratio vegetation index (CamRVI). GPP is derived from eddy covariance flux tower measurement. Then, we extracted PTDs and their uncertainty from different VIs and GPP. The consistency between structural (VIs) and physiological (GPP) phenology was then evaluated. CamNIRv is best at representing the PTDs of GPP during the Green-up period, while CamNDVI is best during the Dry-down period. Moreover, CamNIRv outperforms the other VIs in tracking growing season length of GPP. In summary, the results show it is promising to track structural and physiology phenology of seasonally dry Mediterranean ecosystem using near-infrared-enabled PhenoCam. We suggest using multiple VIs to better represent the variation of GPP. 

   more » « less
3. HP-LSP: A reference of land surface phenology from fused Harmonized Landsat and Sentinel-2 with PhenoCam data

   https://doi.org/10.1038/s41597-023-02605-1  

   Tran, Khuong H. ; Zhang, Xiaoyang ; Ye, Yongchang ; Shen, Yu ; Gao, Shuai ; Liu, Yuxia ; Richardson, Andrew ( December 2023 , Scientific Data)

   Land surface phenology (LSP) products are currently of large uncertainties due to cloud contaminations and other impacts in temporal satellite observations and they have been poorly validated because of the lack of spatially comparable ground measurements. This study provided a reference dataset of gap-free time series and phenological dates by fusing the Harmonized Landsat 8 and Sentinel-2 (HLS) observations with near-surface PhenoCam time series for 78 regions of 10 × 10 km²across ecosystems in North America during 2019 and 2020. The HLS-PhenoCam LSP (HP-LSP) reference dataset at 30 m pixels is composed of: (1) 3-day synthetic gap-free EVI2 (two-band Enhanced Vegetation Index) time series that are physically meaningful to monitor the vegetation development across heterogeneous levels, train models (e.g., machine learning) for land surface mapping, and extract phenometrics from various methods; and (2) four key phenological dates (accuracy ≤5 days) that are spatially continuous and scalable, which are applicable to validate various satellite-based phenology products (e.g., global MODIS/VIIRS LSP), develop phenological models, and analyze climate impacts on terrestrial ecosystems.

    

   more » « less
4. Detecting Recent Crop Phenology Dynamics in Corn and Soybean Cropping Systems of Kentucky

   https://doi.org/10.3390/rs13091615  

   Yang, Yanjun ; Tao, Bo ; Liang, Liang ; Huang, Yawen ; Matocha, Chris ; Lee, Chad D. ; Sama, Michael ; Masri, Bassil El ; Ren, Wei ( May 2021 , Remote Sensing)

   null (Ed.)

   Accurate phenological information is essential for monitoring crop development, predicting crop yield, and enhancing resilience to cope with climate change. This study employed a curve-change-based dynamic threshold approach on NDVI (Normalized Differential Vegetation Index) time series to detect the planting and harvesting dates for corn and soybean in Kentucky, a typical climatic transition zone, from 2000 to 2018. We compared satellite-based estimates with ground observations and performed trend analyses of crop phenological stages over the study period to analyze their relationships with climate change and crop yields. Our results showed that corn and soybean planting dates were delayed by 0.01 and 0.07 days/year, respectively. Corn harvesting dates were also delayed at a rate of 0.67 days/year, while advanced soybean harvesting occurred at a rate of 0.05 days/year. The growing season length has increased considerably at a rate of 0.66 days/year for corn and was shortened by 0.12 days/year for soybean. Sensitivity analysis showed that planting dates were more sensitive to the early season temperature, while harvesting dates were significantly correlated with temperature over the entire growing season. In terms of the changing climatic factors, only the increased summer precipitation was statistically related to the delayed corn harvesting dates in Kentucky. Further analysis showed that the increased corn yield was significantly correlated with the delayed harvesting dates (1.37 Bu/acre per day) and extended growing season length (1.67 Bu/acre per day). Our results suggested that seasonal climate change (e.g., summer precipitation) was the main factor influencing crop phenological trends, particularly corn harvesting in Kentucky over the study period. We also highlighted the critical role of changing crop phenology in constraining crop production, which needs further efforts for optimizing crop management practices. 

   more » « less
5. Springing forward: Migrating songbirds catch up with the start of spring in North America

   https://doi.org/10.1111/1365-2656.14025  

   Nemes, Claire E. ; Marra, Peter P. ; Zenzal, Jr, Theodore J. ; Collins, Samantha A. ; Dossman, Bryant C. ; Gerson, Alexander R. ; Gómez, Camila ; González, Ana M. ; Gutierrez Ramirez, Mariamar ; Hamer, Sarah A. ; et al ( November 2023 , Journal of Animal Ecology)

   In temperate regions, the annual pattern of spring onset can be envisioned as a ‘green wave’ of emerging vegetation that moves across continents from low to high latitudes, signifying increasing food availability for consumers.

   Many herbivorous migrants ‘surf’ such resource waves, timing their movements to exploit peak vegetation resources in early spring. Although less well studied at the individual level, secondary consumers such as insectivorous songbirds can track vegetation phenology during migration as well.

   We hypothesized that four species of ground‐foraging songbirds in eastern North America—two warblers and two thrushes—time their spring migrations to coincide with later phases of vegetation phenology, corresponding to increased arthropod prey, and predicted they would match their migration rate to the green wave but trail behind it rather than surfing its leading edge.

   We further hypothesized that the rate at which spring onset progresses across the continent influences bird migration rates, such that individuals adjust migration timing within North America to phenological conditions they experienceen route.

   To test our hypotheses, we used a continent‐wide automated radio telemetry network to track individual songbirds on spring migration between the U.S. Gulf Coast region and northern locations closer to their breeding grounds.

   We measured vegetation phenology using two metrics of spring onset, the spring index first leaf date and the normalized difference vegetation index (NDVI), then calculated the rate and timing of spring onset relative to bird detections.

   All individuals arrived in the southeastern United States well after local spring onset. Counter to our expectations, we found that songbirds exhibited a ‘catching up’ pattern: Individuals migrated faster than the green wave of spring onset, effectively closing in on the start of spring as they approached breeding areas.

   While surfing of resource waves is a well‐documented migration strategy for herbivorous waterfowl and ungulates, individual songbirds in our study migrated faster than the green wave and increasingly caught up to its leading edgeen route.

   Consequently, songbirds experience a range of vegetation phenophases while migrating through North America, suggesting flexibility in their capacity to exploit variable resources in spring.

    

   more » « less