UAV-based thermography reveals spatial and temporal variability of evapotranspiration from a tropical rainforest

Evapotranspiration (ET) from tropical forests plays a significant role in regulating the climate system. Forests are diverse ecosystems, encompass heterogeneous site conditions and experience seasonal fluctuations of rainfall. Our objectives were to quantify ET from a tropical rainforest using high-resolution thermal images and a simple modeling framework. In lowland Sumatra, thermal infrared (TIR) images were taken from an uncrewed aerial vehicle (UAV) of upland and riparian sites during both dry and wet seasons. We predicted ET from land surface temperature data retrieved from the TIR images by applying the DATTUTDUT energy balance model. We further compared the ET estimates to ground-based sap flux measurements for selected trees and assessed the plot-level spatial and temporal variability of ET across sites and seasons. Average ET across sites and seasons was 0.48 mm h–1, which is comparable to ET from a nearby commercial oil palm plantation where this method has been validated against eddy covariance measurements. For given trees, a positive correlation was found between UAV-based ET and tree transpiration derived from ground-based sap flux measurements, thereby corroborating the observed spatial patterns. Evapotranspiration at upland sites was 11% higher than at riparian sites across all seasons. The heterogeneity of ET was lower at upland sites than at riparian sites, and increased from the dry season to the wet season. This seasonally enhanced ET variability can be an effect of local site conditions including partial flooding and diverse responses of tree species to moisture conditions. These results improve our understanding of forest-water interactions in tropical forests and can aid the further development of vegetation-atmosphere models. Further, we found that UAV-based thermography using a simple, energy balance modeling scheme is a promising method for ET assessments of natural (forest) ecosystems, notably in data scarce regions of the world

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

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 (<jats:italic>R</jats:italic><jats:sup>2</jats:sup> = 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

Airborne Tree Crown Detection for Predicting Spatial Heterogeneity of Canopy Transpiration in a Tropical Rainforest

Tropical rainforests comprise complex 3D structures and encompass heterogeneous site conditions; their transpiration contributes to climate regulation. The objectives of our study were to test the relationship between tree water use and crown metrics and to predict spatial variability of canopy transpiration across sites. In a lowland rainforest of Sumatra, we measured tree water use with sap flux techniques and simultaneously assessed crown metrics with drone-based photogrammetry. We observed a close linear relationship between individual tree water use and crown surface area (R2 = 0.76, n = 42 trees). Uncertainties in predicting stand-level canopy transpiration were much lower using tree crown metrics than the more conventionally used stem diameter. 3D canopy segmentation analyses in combination with the tree crown–water use relationship predict substantial spatial heterogeneity in canopy transpiration. Among our eight study plots, there was a more than two-fold difference, with lower transpiration at riparian than at upland sites. In conclusion, we regard drone-based canopy segmentation and crown metrics to be very useful tools for the scaling of transpiration from tree- to stand-level. Our results indicate substantial spatial variation in crown packing and thus canopy transpiration of tropical rainforests

Predicting tree sap flux and stomatal conductance from drone-recorded surface temperatures in a mixed agroforestry system — a machine learning approach

Plant transpiration is a key element in the hydrological cycle. Widely used methods for its assessment comprise sap flux techniques for whole-plant transpiration and porometry for leaf stomatal conductance. Recently emerging approaches based on surface temperatures and a wide range of machine learning techniques offer new possibilities to quantify transpiration. The focus of this study was to predict sap flux and leaf stomatal conductance based on drone-recorded and meteorological data and compare these predictions with in-situ measured transpiration. To build the prediction models, we applied classical statistical approaches and machine learning algorithms. The field work was conducted in an oil palm agroforest in lowland Sumatra. Random forest predictions yielded the highest congruence with measured sap flux (r$^2$ = 0.87 for trees and r$^2$ = 0.58 for palms) and confidence intervals for intercept and slope of a Passing-Bablok regression suggest interchangeability of the methods. Differences in model performance are indicated when predicting different tree species. Predictions for stomatal conductance were less congruent for all prediction methods, likely due to spatial and temporal offsets of the measurements. Overall, the applied drone and modelling scheme predicts whole-plant transpiration with high accuracy. We conclude that there is large potential in machine learning approaches for ecological applications such as predicting transpiration

UAV-based thermography reveals spatial and temporal variability of evapotranspiration from a tropical rainforest

Evapotranspiration (ET) from tropical forests plays a significant role in regulating the climate system. Forests are diverse ecosystems, encompass heterogeneous site conditions and experience seasonal fluctuations of rainfall. Our objectives were to quantify ET from a tropical rainforest using high-resolution thermal images and a simple modeling framework. In lowland Sumatra, thermal infrared (TIR) images were taken from an uncrewed aerial vehicle (UAV) of upland and riparian sites during both dry and wet seasons. We predicted ET from land surface temperature data retrieved from the TIR images by applying the DATTUTDUT energy balance model. We further compared the ET estimates to ground-based sap flux measurements for selected trees and assessed the plot-level spatial and temporal variability of ET across sites and seasons. Average ET across sites and seasons was 0.48 mm h-1, which is comparable to ET from a nearby commercial oil palm plantation where this method has been validated against eddy covariance measurements. For given trees, a positive correlation was found between UAV-based ET and tree transpiration derived from ground-based sap flux measurements, thereby corroborating the observed spatial patterns. Evapotranspiration at upland sites was 11 and increased from the dry season to the wet season. This seasonally enhanced ET variability can be an effect of local site conditions including partial flooding and diverse responses of tree species to moisture conditions. These results improve our understanding of forest-water interactions in tropical forests and can aid the further development of vegetation-atmosphere models. Further, we found that UAV-based thermography using a simple, energy balance modeling scheme is a promising method for ET assessments of natural (forest) ecosystems, notably in data scarce regions of the world.Peer reviewe

Complex canopy structures control tree transpiration : A study based on 3D modelling in a tropical rainforest

Tropical rainforests are rich in tree species and comprise complex canopy structures. Transpiration by forest trees is a major hydrological flux which contributes to climate regulation. We explored the role of forest canopy structure on tree transpiration in a tropical rainforest on Sumatra, Indonesia. Drone-based photogrammetry and the structure from motion technique were used to compute high-resolution 3D point clouds and derive forest and tree structural variables. Transpiration of differently sized and vertically positioned trees was assessed with sap flux measurements. Per-tree transpiration rates increased linearly with several variables related to crown dimension and were further enhanced by top-heavy crown shape, dense leafage and increasing canopy openness as assessed with the gap light index. Under given environmental conditions, the two variables crown volume and top-heaviness explained 74% of the observed variance in per-tree transpiration. Transpiration rates per unit crown dimension were highest for small crowns, decreased non-linearly with increasing crown dimension and were little affected by other analysed structural variables. Our study underlines the potential of 3D point cloud analyses for accurately determining the structure of complex forest canopies and thus better understanding and predicting transpiration rates of differently positioned trees.Peer reviewe

Sap flow and leaf gas exchange response to a drought and heatwave in urban green spaces in a Nordic city

Urban vegetation plays a role in offsetting urban CO2 emissions, mitigating heat through tree transpiration and shading, and acting as deposition surfaces for pollutants. The frequent occurrence of heatwaves and of concurrent drought conditions significantly disrupts the processes of urban trees, particularly their photosynthesis and transpiration rates. Despite the pivotal role of urban tree functioning in delivering essential ecosystem services, the precise nature of their response remains uncertain. We conducted sap flux density (Js) and leaf gas exchange measurements of four tree species (Tilia cordata, Tilia × europaea, Betula pendula, and Malus spp.) located in different urban green areas (Park, Street, Forest, and Orchard) in Helsinki, Finland. Measurements were made over two contrasting summers 2020 and 2021. Summer 2021 experienced a local heatwave and drought, whereas summer 2020 was more typical of Helsinki. In this study, we aimed to understand the responses of urban tree transpiration (measured with sap flux density) and leaf gas exchange to heatwave and drought conditions, and we examined the main environmental drivers controlling the tree transpiration rate during these periods. We observed varying responses of Js during the heatwave period at the four urban sites. When comparing the heatwave and no heatwave periods, a 35 %-67 % increase in Js was observed at the Park, Forest, and Orchard locations, whereas no significant change was seen at the Street site. Our results also showed that Js was higher (31 %-63 %) at all sites under drought conditions compared with non-dry periods. The higher Js values during the heatwave and dry periods were mainly driven by the high atmospheric demand for evapotranspiration, represented by the high vapor pressure deficit (VPD), suggesting that the trees were not experiencing severe enough heat or drought stress that stomatal control would have decreased transpiration. Accordingly, photosynthetic potential (Amax), stomatal conductance (gs), and transpiration (E) at the leaf level did not change during heatwave and drought periods, excluding the Park site where a significant reduction in gs was seen. VPD explained 55 %-69 % of the variation in the daily mean Js during heatwave and drought periods at all sites. At the Forest site, the increase in Js saturated after a certain VPD level, likely due to low soil water availability during these hot and dry periods. Overall, the heat and drought conditions were untypical of the region but not excessive enough to restrict stomatal control and transpiration, indicating that ecosystem services such as cooling were not at risk.Peer reviewe