Remote sensing of vegetation: principles, techniques and applications. By Hamlyn G. Jones and Robin A Vaughan

Kulawardhana, R. W.

doi:10.1111/j.1654-1103.2011.01319.x

Cited by 6 publications

(2 citation statements)

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“…According to Kulawardhana. [64], the positive correlation of spectral bands with species diversity is due to species-specific differences in pigment content, leaf structure, and canopy structural components such as leaf area index. Therefore, the importance of the visible bands in plant diversity prediction can be justified by the fact that this portion of the spectrum, especially bands in the red and blue, is the major leaf pigment absorption range, which is sensitive to mainly chlorophyll a, and b contents, according to the sensitivity analysis reported by Zhao et al [65].…”

Section: Discussionmentioning

confidence: 99%

Assessing Forest Species Diversity in Ghana’s Tropical Forest Using PlanetScope Data

Njomaba,

Ofori,

Guuroh

et al. 2024

Remote Sensing

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This study utilized a remotely sensed dataset with a high spatial resolution of 3 m to predict species diversity in the Bobiri Forest Reserve (BFR), a moist semi-deciduous tropical forest in Ghana. We conducted a field campaign of tree species measurements to achieve this objective for species diversity estimation. Thirty-five field plots of 50 m × 20 m were established, and the most dominant tree species within the forest were identified. Other measurements, such as diameter at breast height (DBH ≥ 5 cm), tree height, and each plot’s GPS coordinates, were recorded. The following species diversity indices were estimated from the field measurements: Shannon–Wiener (H′), Simpson diversity index (D2), species richness (S), and species evenness (J′). The PlanetScope surface reflectance data at 3 m spatial resolution was acquired and preprocessed for species diversity prediction. The spectral/pixel information of all bands, except the coastal band, was extracted for further processing. Vegetation indices (VIs) (NDVI—normalized difference vegetation index, EVI—enhanced vegetation index, SRI—simple ratio index, SAVI—soil adjusted vegetation index, and NDRE—normalized difference red edge index) were also calculated from the spectral bands and their pixel value extracted. A correlation analysis was then performed between the spectral bands and VIs with the species diversity index. The results showed that spectral bands 6 (red) and 2 (blue) significantly correlated with the two main species diversity indices (S and H′) due to their influence on vegetation properties, such as canopy biomass and leaf chlorophyll content. Furthermore, we conducted a stepwise regression analysis to investigate the most important spectral bands to consider when estimating species diversity from the PlanetScope satellite data. Like the correlation results, bands 6 (red) and 2 (blue) were the most important bands to be considered for predicting species diversity. The model equations from the stepwise regression were used to predict tree species diversity. Overall, the study’s findings emphasize the relevance of remotely sensed data in assessing the ecological condition of protected areas, a tool for decision-making in biodiversity conservation.

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Section: Discussionmentioning

confidence: 99%

Assessing Forest Species Diversity in Ghana’s Tropical Forest Using PlanetScope Data

Njomaba,

Ofori,

Guuroh

et al. 2024

Remote Sensing

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“…With all else equal, higher transpiration rates (a greater stomatal conductance) decrease canopy temperature (CT) via evaporative cooling; therefore, CT measurements have been used as a fast surrogate measure for stomatal conductance (e.g., Rebetzke et al, 2013). Additionally, since photosynthetic gas exchange and stomatal conductance are intertwined, CT may provide an indirect measurement of photosynthetic rate (Amani et al, 1996;Fischer et al, 1998;Jones and Vaughan, 2011). Recently, CT measurements have been acquired using airborne imaging thermography, enabling the measurement of large experiments in short time, which greatly increased repeatability of measurements as compared to plot-by-plot measurements using handheld thermometers (e.g., Deery et al, 2019Deery et al, , 2016Perich et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Thermal imaging can reveal variation in stay-green functionality of wheat canopies under temperate conditions

Anderegg,

Kirchgessner,

Aasen

et al. 2023

Preprint

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Canopy temperature (CT) is often interpreted as representing leaf activity traits such as photosynthetic rates, gas exchange rates, or stomatal conductance. Accordingly, CT measurements may provide a basis for high throughput assessments of the productivity of wheat canopies during early grain filling, which would allow distinguishing functional from dysfunctional stay-green. However, whereas the usefulness of CT as a fast surrogate measure of sustained vigor under soil drying is well established, its potential to quantify leaf activity traits under high-yielding conditions is less clear. To better understand sensitivity limits of CT measurements under high yielding conditions, we generated within-genotype variability in stay-green functionality by means of differential short-term pre-anthesis canopy shading that modified the sink:source balance. We quantified the effects of these modifications on stay-green properties through a combination of gold standard physiological measurements of leaf activity and newly developed methods for organ-level senescence monitoring based on timeseries of high-resolution imagery and deep-learning-based semantic image segmentation. In parallel, we monitored CT by means of a pole-mounted thermal camera that delivered continuous, ultra-high temporal resolution CT data. Our results show that differences in leaf activity stemming from differences in stay-green functionality translate into measurable differences in CT in the absence of major confounding factors. Differences amounted to approximately 0.8°C and 1.5°C for a very high-yielding source-limited genotype, and a medium-yielding sink-limited genotype, respectively. The gradual nature of the effects of shading on CT during the stay-green phase underscore the importance of a high measurement frequency and a time-integrated analysis of CT, whilst modest effect sizes confirm the importance of restricting screenings to a limited range of morphological and phenological diversity.

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A study on deep learning algorithm performance on weed and crop species identification under different image background

Sunil

Koparan

Ahmed

et al. 2022

Artificial Intelligence in Agriculture

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Remote sensing of vegetation: principles, techniques and applications. By Hamlyn G. Jones and Robin A Vaughan

Cited by 6 publications

References 0 publications

Assessing Forest Species Diversity in Ghana’s Tropical Forest Using PlanetScope Data

Assessing Forest Species Diversity in Ghana’s Tropical Forest Using PlanetScope Data

Thermal imaging can reveal variation in stay-green functionality of wheat canopies under temperate conditions

A study on deep learning algorithm performance on weed and crop species identification under different image background

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