Photography with small unmanned aircraft systems (
This content will become publicly available on November 8, 2024
Reduced light is one of the primary threats to seagrass meadows in the coming decades, with reduced light reaching the benthos due to eutrophication. We assessed a multispectral photography technique using near‐infrared photography to estimate chlorophyll content in the seagrass
- NSF-PAR ID:
- 10485346
- Publisher / Repository:
- Wiley Blackwell (John Wiley & Sons)
- Date Published:
- Journal Name:
- Limnology and Oceanography: Methods
- Volume:
- 22
- Issue:
- 1
- ISSN:
- 1541-5856
- Format(s):
- Medium: X Size: p. 25-33
- Size(s):
- ["p. 25-33"]
- Sponsoring Org:
- National Science Foundation
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Abstract sUAS ) offers opportunities for researchers to better understand habitat selection in wildlife, especially for species that select habitat from an aerial perspective (e.g., many bird species). The growing number of commercialsUAS being flown by recreational users represents a potentially valuable source of data for documenting and studying wildlife habitat. We used a commercially available quadcoptersUAS with a visible spectrum camera to classify habitat for American Kestrels (Falco sparverius ; Aves), as well as to evaluate aspects of image processing and postprocessing relevant to a simple habitat analysis using citizen science photography. We investigated inter–observer repeatability of habitat classification, effectiveness of cross‐image classification and Gaussian filtering, and sensitivity to classification resolution. We photographed vegetation around nests from both 25 m and 50 m above takeoff elevation, and analyzed images via maximum likelihood supervised classification. Our results indicate that commercial off‐the‐shelfsUAS photography can distinguish between grass, herbaceous, woody, bare ground, and human‐modified cover classes with good (kappa > 0.6) or strong (kappa > 0.8) accuracy using a 0.25 m2minimum patch size for aggregation. There was inter‐subject variability in designating training samples, but high repeatability of supervised classification accuracy. Gaussian filtering reduced classification accuracy, while coarser classification resolution out‐performed finer resolution due to “speckling noise.” Image self‐classification significantly outperformed cross‐image classification. Mean classification accuracy metrics (kappa values) across different photo heights differed little, but, importantly, the rank order of images differed noticeably. -
Abstract Accurate estimation of terrestrial gross primary productivity (
GPP ) remains a challenge despite its importance in the global carbon cycle. Chlorophyll fluorescence (ChlF) has been recently adopted to understand photosynthesis and its response to the environment, particularly with remote sensing data. However, it remains unclear how ChlF and photosynthesis are linked at different spatial scales across the growing season. We examined seasonal relationships between ChlF and photosynthesis at the leaf, canopy, and ecosystem scales and explored how leaf‐level ChlF was linked with canopy‐scale solar‐induced chlorophyll fluorescence (SIF ) in a temperate deciduous forest at Harvard Forest, Massachusetts,USA . Our results show that ChlF captured the seasonal variations of photosynthesis with significant linear relationships between ChlF and photosynthesis across the growing season over different spatial scales (R 2 = 0.73, 0.77, and 0.86 at leaf, canopy, and satellite scales, respectively;P < 0.0001). We developed a model to estimateGPP from the tower‐based measurement ofSIF and leaf‐level ChlF parameters. The estimation ofGPP from this model agreed well with flux tower observations ofGPP (R 2 = 0.68;P < 0.0001), demonstrating the potential ofSIF for modelingGPP . At the leaf scale, we found that leafF q ’ /F m ’ , the fraction of absorbed photons that are used for photochemistry for a light‐adapted measurement from a pulse amplitude modulation fluorometer, was the best leaf fluorescence parameter to correlate with canopySIF yield (SIF /APAR ,R 2 = 0.79;P < 0.0001). We also found that canopySIF andSIF ‐derivedGPP (GPPSIF ) were strongly correlated to leaf‐level biochemistry and canopy structure, including chlorophyll content (R 2 = 0.65 for canopyGPPSIF and chlorophyll content;P < 0.0001), leaf area index (LAI ) (R 2 = 0.35 for canopyGPPSIF andLAI ;P < 0.0001), and normalized difference vegetation index (NDVI ) (R 2 = 0.36 for canopyGPPSIF andNDVI ;P < 0.0001). Our results suggest that ChlF can be a powerful tool to track photosynthetic rates at leaf, canopy, and ecosystem scales. -
Abstract High temperature and accompanying high vapor pressure deficit often stress plants without causing distinctive changes in plant canopy structure and consequential spectral signatures. Sun‐induced chlorophyll fluorescence (SIF), because of its mechanistic link with photosynthesis, may better detect such stress than remote sensing techniques relying on spectral reflectance signatures of canopy structural changes. However, our understanding about physiological mechanisms of SIF and its unique potential for physiological stress detection remains less clear. In this study, we measured SIF at a high‐temperature experiment, Temperature Free‐Air Controlled Enhancement, to explore the potential of SIF for physiological investigations. The experiment provided a gradient of soybean canopy temperature with 1.5, 3.0, 4.5, and 6.0°C above the ambient canopy temperature in the open field environments. SIF yield, which is normalized by incident radiation and the fraction of absorbed photosynthetically active radiation, showed a high correlation with photosynthetic light use efficiency (
r = 0.89) and captured dynamic plant responses to high‐temperature conditions. SIF yield was affected by canopy structural and plant physiological changes associated with high‐temperature stress (partial correlationr = 0.60 and −0.23). Near‐infrared reflectance of vegetation, only affected by canopy structural changes, was used to minimize the canopy structural impact on SIF yield and to retrieve physiological SIF yield (ΦF) signals. ΦFfurther excludes the canopy structural impact than SIF yield and indicates plant physiological variability, and we found that ΦFoutperformed SIF yield in responding to physiological stress (r = −0.37). Our findings highlight that ΦFsensitively responded to the physiological downregulation of soybean gross primary productivity under high temperature. ΦF, if reliably derived from satellite SIF, can support monitoring regional crop growth and different ecosystems' vegetation productivity under environmental stress and climate change. -
Premise Light is critical in the ability of plants to accumulate chlorophyll. When exposed to far‐red (
FR ) light and then grown in white light in the absence of sucrose, wild‐type seedlings fail to green in a response known as theFR block of greening (BOG ). This response is controlled by phytochrome A through repression of protochlorophyllide reductase‐encoding (POR ) genes byFR light coupled with irreversible plastid damage. Sigma (SIG ) factors are nuclear‐encoded proteins that contribute to plant greening and plastid development through regulating gene transcription in chloroplasts and impacting retrograde signaling from the plastid to nucleus.SIG s are regulated by phytochromes, and the expression of someSIG factors is reduced in phytochrome mutant lines, includingphyA . Given the association of phyA with theFR BOG and its regulation ofSIG factors, we investigated the potential regulatory role ofSIG factors in theFR BOG response.Methods We examined
FR BOG responses insig mutants, phytochrome‐deficient lines, and mutant lines for several phy‐associated factors. We quantified chlorophyll levels and examined expression of keyBOG ‐associated genes.Results Among six
sig mutants, only thesig6 mutant significantly accumulated chlorophyll afterFR BOG treatment, similar to thephyA mutant.SIG 6 appears to control protochlorophyllide accumulation by contributing to the regulation of tetrapyrrole biosynthesis associated with glutamyl‐tRNA reductase (HEMA 1) function, select phytochrome‐interacting factor genes (PIF4 andPIF6 ), andPENTA1 , which regulatesPORA mRNA translation afterFR exposure.Conclusions Regulation of
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