Conceptual Elements and Heuristics from Complexity Paradigm Suitable to the Study of Evapotranspiration at the Landscape Level

Coronel, Claudia; Silva, Omar Tapia; Hernández, G.; Madrigal, José Manuel; Rosales, Edgar; Toledo, A. Szanto de; Galeana, Mauricio; Caloca, Alejandra López; Silvan, José Luis

doi:10.5772/14056

Cited by 1 publication

(2 citation statements)

References 83 publications

(101 reference statements)

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“…To examine the influence of single variables on pixel‐level ET, we applied linear regressions between ET and each of the 16 potential predictor variables, separately for each study plot. For visualization in scatterplots, we selected the variables LAD and maximum vegetation height (Height_abs_max) due to their importance for modelling forest structure (Andersen et al, 2005; McGaughey, 2018; Mutlu, 2006) and their reported strong influence on ET in studies at larger spatial scales (Coronel et al, 2011).…”

Section: Methodsmentioning

confidence: 99%

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Combining UAV thermography, point cloud analysis and machine learning for assessing small‐scale evapotranspiration patterns in a tropical rainforest

Cortés‐Molino,

Valdés‐Uribe,

Ellsäßer

et al. 2023

Ecohydrology

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Microclimate and vegetation structure control evapotranspiration (ET) from land surfaces at stand and landscape scales. Tropical rainforests are among the most diverse and complex terrestrial ecosystems, harbouring vast plant and animal species throughout their dense multistory canopy. They contribute substantially to global precipitation through their high ET. However, there is little information about ET influences at very small spatial scales under given climatic conditions. In a tropical rainforest on Sumatra, we studied the relationship between pixel‐level ET as derived from high‐resolution (~10 cm), near‐surface thermography from an unmanned aerial vehicle (UAV) and canopy structure as derived from red–green–blue (RGB) image and three‐dimensional (3D) point cloud analyses. The 16 derived potential predictors encompassed vegetation height, height variability, vegetation density and reflectance variables. Using regression models, several of the studied variables had a significant linear relationship with ET, but the explained variance was only marginal. However, applying a random forest algorithm including forward feature selection and target oriented cross validation explained substantial parts of the pixel‐level variance in ET (R2 = 0.56–0.65), thus indicating multiple non‐linear relationships with interactions among predictor variables. Therein, green leaf index, leaf area density and vegetation height were often the most important variables for the prediction outcome, but their sequence varied among the four study plots. Overall, combining canopy structure variables derived from RGB photogrammetry explained relatively large parts of spatial ET variations. Our study thus indicates the large potential of combining UAV‐based thermography and photogrammetry techniques with machine learning approaches to better understand ET but also suggests that more work remains to be done in explaining ET patterns at very small spatial scales.

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Section: Methodsmentioning

confidence: 99%

“…Crown leaf density and vertical vegetation structure (e.g. tree or crown height) have been proposed as key regional controls of ET (Coronel et al, 2011), while studies at the local level also report strong influences of canopy conductance and plant health (e.g. Marques et al, 2020; Nazarbakhsh et al, 2019).…”

Section: Introductionmentioning

confidence: 99%