Prediction of Macronutrients at the Canopy Level Using Spaceborne Imaging Spectroscopy and LiDAR Data in a Mixedwood Boreal Forest

Predicting soil nutrient contents using Landsat OLI satellite images in rain-fed agricultural lands, northwest of Iran

Soil nutrients are the key factors in soil fertility, which have important roles in plant growth. Determining soil nutrient contents, including macro and micronutrients, is of crucial importance in agricultural productions. Conventional laboratory techniques for determining soil nutrients are expensive and time-consuming. This research was aimed to develop linear regression (LR) models for remote sensing of total nitrogen (TN) (mg/kg), available phosphorous (AP) (mg/kg), available potassium (AK) (mg/kg), and micronutrients such as iron (Fe) (mg/kg), manganese (Mn) (mg/kg), zinc (Zn) (mg/kg), and copper (Cu) (mg/kg) extracted by DTPA in rain-fed agricultural lands in the northwest of Iran. First, 101 soil samples were collected from 0-30 cm of these lands and analyzed for selected nutrient contents. Then a linear regression along with principal component analysis was conducted to correlate soil nutrient contents with reflectance data of different Landsat OLI bands. Finally, the spatial distributions of soil nutrients were drawn. The results showed that there were linear relationships between soil nutrient contents and standardized PC₁ (Z_PC1). The highest significant determination coefficient with an R² value of 0.46 and the least relative error (%) value of 11.97% were observed between TN and Z_PC1. The accuracy of the other LR's developed among other soil nutrient contents and remotely sensed data was relatively lower than that obtained for TN. According to the results obtained from this study, although remote sensing techniques may quickly assess soil nutrients, new techniques, technologies, and models may be needed to have a more accurate prediction of soil nutrients.

Mapping the Leaf Economic Spectrum across West African Tropical Forests Using UAV-Acquired Hyperspectral Imagery

The leaf economic spectrum (LES) describes a set of universal trade-offs between leaf mass per area (LMA), leaf nitrogen (N), leaf phosphorus (P) and leaf photosynthesis that influence patterns of primary productivity and nutrient cycling. Many questions regarding vegetation-climate feedbacks can be addressed with a better understanding of LES traits and their controls. Remote sensing offers enormous potential for generating large-scale LES trait data. Yet so far, canopy studies have been limited to imaging spectrometers onboard aircraft, which are rare, expensive to deploy and lack fine-scale resolution. In this study, we measured VNIR (visible-near infrared (400–1050 nm)) reflectance of individual sun and shade leaves in 7 one-ha tropical forest plots located along a 1200–2000 mm precipitation gradient in West Africa. We collected hyperspectral imaging data from 3 of the 7 plots, using an octocopter-based unmanned aerial vehicle (UAV), mounted with a hyperspectral mapping system (450–950 nm, 9 nm FWHM). Using partial least squares regression (PLSR), we found that the spectra of individual sun leaves demonstrated significant (p < 0.01) correlations with LMA and leaf chemical traits: r2 = 0.42 (LMA), r2 = 0.43 (N), r2 = 0.21 (P), r2 = 0.20 (leaf potassium (K)), r2 = 0.23 (leaf calcium (Ca)) and r2 = 0.14 (leaf magnesium (Mg)). Shade leaf spectra displayed stronger relationships with all leaf traits. At the airborne level, four of the six leaf traits demonstrated weak (p < 0.10) correlations with the UAV-collected spectra of 58 tree crowns: r2 = 0.25 (LMA), r2 = 0.22 (N), r2 = 0.22 (P), and r2 = 0.25 (Ca). From the airborne imaging data, we used LMA, N and P values to map the LES across the three plots, revealing precipitation and substrate as co-dominant drivers of trait distributions and relationships. Positive N-P correlations and LMA-P anticorrelations followed typical LES theory, but we found no classic trade-offs between LMA and N. Overall, this study demonstrates the application of UAVs to generating LES information and advancing the study and monitoring tropical forest functional diversity.