Remote Sensing of Grassland Functional Traits

Hyperspectral Remote Sensing of Grassland Functional Traits

On experimental grassland sites in Germany, Capolupo et al. (2015) used a UAV mounted hyperspectral camera and ground-based surveys to examine a range of physical and chemical traits (e.g., height, biomass, crude protein, nitrogen, potassium, etc). To estimate these traits, the authors used two methods, partial least squares regression (PLSR) and vegetation indices. PLSR performed well for both physical and chemical traits, while vegetation indices worked well only with physical traits. In a separate experimental barley site in Germany, Aasen et al. (2015) combined 3D with hyperspectral imagery for precision agriculture. Plant height, chlorophyll, leaf area index (LAI) and biomass were measured with r2 values of 0.7, 0.52, 0.32 and 0.29, respectively. Airborne hyperspectral surveys were used over Swiss alpine grasslands, in combination with ground surveys, to examine links between community traits, functional traits and spectra with PLSR. The authors noted some inconsistent results with plant life and growth forms, but functional type modelling produced more consistent results (Schweiger et al., 2017). In a barley test site in Finland, Näsi et al. (2018) equipped a UAV with hyperspectral and RGB cameras. The authors combined the digital surface model and digital terrain model, generated with using SfM, with the hyperspectral information, estimating dry and fresh matter, and nitrogen content. The 3D data combined with RGB information was nearly as accurate as hyperspectral data for biomass, but hyperspectral imaging performed better for nitrogen content. Wang et al. (2019) used ground sampling and airborne hyperspectral surveys to map foliar functional traits across a grassland experimental site in Minnesota. They used PLSR and gaussian processes regression (GPR) for modelling, uncertainty and performance. Both regression methods produced similar results, with leaf mass per area, soluble cell contents, hemicellulose and cellulose all producing r2values > 0.8, while models for the contents of lignin, nitrogen and some pigments performed more poorly. Across eight grassland sites in Northern Germany, Wijesingha et al. (2020) attempted to predict forage quality by mapping crude protein and acid detergent fibre using hyperspectral airborne surveys. Five predictive modelling methods were assessed PLSR, random forest regression, GPR, support vector regression and cubist regression. Support vector regression performed best for crude protein, while cubist regression performed best for acid detergent fibre. In an Inner Mongolian monoculture test site, Zhao et al. (2021a) used a UAV mounted spectrometer to measure chemical traits (carbon, nitrogen, phosphorus, lignin, cellulose, and chlorophyll a and b). They successfully measured numerous functional traits and noted that retrieval worked better on an area basis rather than mass basis. Certain traits could also be used effectively as predictors of AGB across the monoculture sites. In mixed grasslands on the Tibetan Plateau, UAV hyperspectral surveys were used by Zhang et al. (2022) to map functional traits. Algorithms used were PLSR, the generic algorithm integrated with the PLSR, random forest (RF) and extreme gradient boosting (XGBoost). Chlorophyll a, chlorophyll b, carotenoid content, starch content, specific leaf area and leaf thickness were estimated well (r2 values between 0.64 and 0.8), while nitrogen content, phosphorus content, plant height and leaf dry matter content were modelled with lower accuracy (r2 values between 0.3 and 0.54). Finally, Gholizadeh et al. (2022) used aerial spectroscopy (1 m resolution) to map 12 functional traits and use these to differentiate an invasive grassland species, Lespedeza cuneata , from other species in a tallgrass prairie site in Oklahoma. They achieved an accuracy on 94%, showing that functional trait measurements can be used to identify invasive species. However, the accuracy was lower in species-rich grasslands.

Multispectral Remote Sensing of Grassland Functional Traits

On the Qinghai-Tibetan Steppe, Li et al. (2018) used Landsat 8 and Sentinel 2 to map plant functional traits at the community level - canopy chlorophyll, specific plant area and plant dry matter content. The authors used field sampling in combination with vegetation indices and statistical modelling, with google earth engine, and achieved moderately good results (r2 from 0.22 to 0.53). Imran et al. (2020) used ground-based hyperspectral measurements to simulate Sentinel 2 and 3 imagery, utilising red edge and NIR vegetation indices to map LAI in grasslands in northern Italy and Austria. They found that LAI retrieval is strongly influenced by plant traits, physical and chemical (e.g., AGB, leaf angle distribution, brown pigment content and chlorophyll content). Over an experimental site in northern Germany, Grüner at al. (2020) used UAV multispectral data, with PLSR and RF regression, for predicting aboveground biomass and nitrogen fixation in legume-grass mixtures. While consistent estimates of biomass were achieved using RF and the results for nitrogen fixation were also strong, the most effective regression method depended on the specific legume/grass proportions in the test site. In a subsequent study, at the same location, Grüner at al. (2021) combined a terrestrial laser scan survey with a multispectral UAV survey (including texture analysis) to measure fresh and dry matter (biomass) and nitrogen fixation in legume grass mixtures. The fusion approach proved to be a significantly better predictor, overcoming the limitation of each separate sensor. For nitrogen fixation, the multispectral sensor had a relative root mean squared error of prediction (rRMSEP) of 17.64%, with a rRMSEP of 20.07% for canopy surface height and 14.4% for the sensor fusion. Rakotoarivony et al. (2023) used field sampling to identify unique functional traits that distinguishesL. cuneata from the native grassland species. Next, airborne hyperspectral data was used to identify the vegetation indices most closely related to these traits, and PlanetScope multispectral satellite imagery was then used to identify and map L. cuneata across a 47 km2 region of U.S., with an accuracy of over 80%. Across three grassland sites in Inner Mongolia, Zhao et al. (2024) used field sampling to identify 13 functional traits and associated these traits with leaf spectra through PLSR. PLSR was also applied to twelve bands, 30 vegetation indices and convex hull volume from Sentinel 2 imagery to map function diversity on a large scale. All but one of the functional traits (carbon) were predicted reasonably well from the Sentinel 2 imagery, with r2 values of between 0.32 and 0.82.