This paper demonstrates the ability to generate quantitative remote sensing products by means of an unmanned aerial vehicle (UAV) equipped with one unaltered and one near infrared-modified commercial off-the-shelf (COTS) camera. Radiometrically calibrated orthomosaics were generated for 17 dates, from which digital numbers were corrected to surface reflectance and to normalized difference vegetation index (NDVI). Validation against ground measurements showed that 84%-90% of the variation in the ground reflectance and 95%-96% of the variation in the ground NDVI could be explained by the UAV-retrieved reflectance and NDVI, respectively. Comparisons against Landsat 8 data showed relationships of $0.73\leq R^{2} \geq 0.84$ for reflectance and $0.86\leq R^{2} \geq 0.89$ for NDVI. It was not possible to generate a fully consistent time series of reflectance, due to variable illumination conditions during acquisition on some dates. However, the calculation of NDVI resulted in a more stable UAV time series, which was consistent with a Landsat series of NDVI extracted over a deciduous and evergreen woodland. The results confirm that COTS cameras, following calibration, can yield accurate reflectance estimates (under stable within-flight illumination conditions), and that consistent NDVI time series can be acquired in very variable illumination conditions. Such methods have significant potential in providing flexible, low-cost approaches to vegetation monitoring at fine spatial resolution and for user-controlled revisit periods.

Continuous and dense time series of satellite remote sensing data are needed for several land monitoring applications, including vegetation phenology, in-season crop assessments, and improving land use and land cover classification. Supporting such applications at medium to high spatial resolution may be challenging with a single optical satellite sensor, as the frequency of good-quality observations can be low. To optimize good-quality data availability, some studies propose harmonized databases. This work aims at developing an ‘all-in-one’ Google Earth Engine (GEE) web-based workflow to produce harmonized surface reflectance data from Landsat-7 (L7) ETM+, Landsat-8 (L8) OLI, and Sentinel-2 (S2) MSI top of atmosphere (TOA) reflectance data. Six major processing steps to generate a new source of near-daily Harmonized Landsat and Sentinel (HLS) reflectance observations at 30 m spatial resolution are proposed and described: band adjustment, atmospheric correction, cloud and cloud shadow masking, view and illumination angle adjustment, co-registration, and reprojection and resampling. The HLS is applied to six equivalent spectral bands, resulting in a surface nadir BRDF-adjusted reflectance (NBAR) time series gridded to a common pixel resolution, map projection, and spatial extent. The spectrally corresponding bands and derived Normalized Difference Vegetation Index (NDVI) were compared, and their sensor differences were quantified by regression analyses. Examples of HLS time series are presented for two potential applications: agricultural and forest phenology. The HLS product is also validated against ground measurements of NDVI, achieving very similar temporal trajectories and magnitude of values (R² = 0.98). The workflow and script presented in this work may be useful for the scientific community aiming at taking advantage of multi-sensor harmonized time series of optical data.

Commercial off-the-shelf (COTS) digital cameras on-board unmanned aerial vehicles (UAVs) have the potential to be used as multispectral imaging systems; however, their spectral sensitivity is usually unknown and needs to be either measured or estimated. This paper details a step by step methodology for identifying the spectral sensitivity of modified (to be response to near infra-red wavelengths) and un-modified COTS digital cameras, showing the results of its application for three different models of camera. Six digital still cameras, which are being used as imaging systems on-board different UAVs, were selected to have their spectral sensitivities measured by a monochromator. Each camera was exposed to monochromatic light ranging from 370 nm to 1100 nm in 10 nm steps, with images of each step recorded in RAW format. The RAW images were converted linearly into TIFF images using DCRaw, an open-source program, before being batch processed through ImageJ (also open-source), which calculated the mean and standard deviation values from each of the red-green-blue (RGB) channels over a fixed central region within each image. These mean values were then related to the relative spectral radiance from the monochromator and its integrating sphere, in order to obtain the relative spectral response (RSR) for each of the cameras colour channels. It was found that different un-modified camera models present very different RSR in some channels, and one of the modified cameras showed a response that was unexpected. This highlights the need to determine the RSR of a camera before using it for any quantitative studies.

The aim of this study is to assess the potential of imagery acquired from an Unmanned Aerial Vehicle (UAV) to track seasonal changes in leaf canopy at individual tree level. UAV flights were carried out over a deciduous woodland during the spring season of 2015, from which a temporal series of 5 cm spatial resolution orthophotos was generated. Initial results are presented in this paper. Four trees with different observed Start of Season (SOS) dates were selected to monitor UAV-derived Green Chromatic Coordinate (GCC). The chronological order when sudden increases of GCC values occurred matched with the chronological order of observed SOS. Trees with later observed SOS presented GCC values increasing slowly over time, which were associated with development of understory vegetation. It is concluded that UAV imagery has the potential to track leaf phenology at the individual tree level, but further studies are necessary to better understand this new level of information detected from UAVs.

<!-- *** Custom HTML *** --> LiDAR remote sensing data combined with machine learning (ML) techniques have presented great potential for large-scale modeling of tropical forest attributes. However, the large amount of information that can be derived from an aerial LiDAR survey, summed with the intrinsic heterogeneity of tropical environments (e.g., the Amazon), makes it a challenge to accurately estimate forest biophysical variables. The aim of our work is to investigate the potential and accuracy of different ML techniques and a generalized linear model (GLM) to learn the relationships between LiDAR-derived metrics and forest inventory data for aboveground biomass (AGB) prediction in Amazon forest sites under selective logging regimes. The predictive performance of three ML techniques, namely random forest (RF), support vector machine (SVM), and artificial neural network (ANN), was compared against result from the GLM technique, across 85 sample plots. Interestingly, the GLM retrieved the most accurate estimations of forest AGB (rho Spearman’s coefficient = 0.87), compared with the ML techniques (RF = 0.77, SVM = 0.67, and ANN = 0.50). A number of possible factors affecting such results are listed and discussed in the text, including sample size and number of predictor variables. Continued research is necessary to improve the confidence of AGB estimation, especially over complex forest structures.