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Academy of Finland Academy of Finland: 273806 This study experiments with different combinations of UAV hyperspectral data and LiDAR metrics for classifying eight tree species found in a Brazilian Atlantic Forest remnant, the most degraded Brazilian biome with high fragmentation but with huge structural complexity. The selection of the species was done based on the number of tree samples, which exist in the plot data and in the fact the UAV imagery does not acquire information below the forest canopy. Due to the complexity of the forest, only species that exist in the upper canopy of the remnant were included in the classification. A combination of hyperspectral UAV images and LiDAR point clouds were in the experiment. The hyperspectral images were photogrammetric and radiometric processed to obtain orthomosaics with reflectance factor values. Raw spectra were extracted from the trees, and vegetation indices (VIs) were calculated. Regarding the LiDAR data, both the point cloud—referred to as Peak Returns (PR)—and the full-waveform (FWF) LiDAR were included in this study. The point clouds were processed to normalize the intensities and heights, and different metrics for each data type (PR and FWF) were extracted. Segmentation was preformed semi-automatically using the superpixel algorithm, followed with manual correction to ensure precise tree crown delineation before tree species classification. Thirteen different classification scenarios were tested. The scenarios included spectral features and LiDAR metrics either combined or not. The best result was obtained with all features transformed with principal component analysis with an accuracy of 76%, which did not differ significantly from the scenarios using the raw spectra or VIs with PR or FWF LiDAR metrics. The combination of spectral data with geometric information from LiDAR improved the classification of tree species in a complex tropical forest, and these results can serve to inform management and conservation practices of these forest remnants.

Abstract. Shadows are common in any kind of remote sensing images. Unmanned Aerial Vehicle – UAV with a light camera attached can acquire images illuminated either by direct sunlight or by diffuse light under clouds. Indeed, areas with pixels shaded by clouds must be detected and labelled in order to use this additional information for image analysis. Classification of health and diseased plants in permanent culture as the orange plantation field can present some errors due to tree cast shadow. So, hyperspectral or multispectral image classification can be improved by previous shadow detection. Some FPI hyperspectral camera, designed for agricultural applications is limited in the spectral range between 500 to 900 nm. Wavelengths in the region of blue light and in the SWIR spectral region have physical properties that enable the enhancement of shaded regions in the images. In this work some combinations of different spectral bands were evaluated in order to specify those suitable to detect shadows in agricultural field images. In this sense, considering that vegetation and soil are the two main kind of coverage in an agricultural field, we hypothesized that wavelengths near blue light and the longest near infrared available in the camera range are good choices. In both spectral regions soil and vegetation targets have small spectral differences which contribute to enhance the differences between shaded and illuminated regions in the image. Hyperspectral images acquired with a FPI hyperspectral camera onboard a UAV over a plantation of oranges were used to evaluate these spectral bands. The results showed that the wavelengths of aproximatelly 510 nm and 840 nm available in the FPI camera are the best to detect any type of shadows in the agricultural fields.

The aim of this study was to investigate the capabilities of two date satellite-derived image-based point clouds (IPCs) to estimate forest aboveground biomass (AGB). The data sets used include panchromatic WorldView-2 stereo-imagery with 0.46 m spatial resolution representing 2014 and 2016 and a detailed digital elevation model derived from airborne laser scanning data. Altogether, 332 field sample plots with an area of 256 m(2) were used for model development and validation. Predictors describing forest height, density, and variation in height were extracted from the IPC 2014 and 2016 and used in k-nearest neighbour imputation models developed with sample plot data for predicting AGB. AGB predictions for 2014 (AGB(2014)) were projected to 2016 using growth models (AGB(Projected_2016)) and combined with the AGB estimates derived from the 2016 data (AGB(2016)). AGB prediction model developed with 2014 data was also applied to 2016 data (AGB(2016_pred2014)). Based on our results, the change in the 90(th) percentile of height derived from the WorldView-2 IPC was able to characterize forest height growth between 2014 and 2016 with an average growth of 0.9 m. Features describing canopy cover and variation in height derived from the IPC were not as consistent. The AGB(2016) had a bias of -7.5% (-10.6 Mg ha(-1)) and root mean square error (RMSE) of 26.0% (36.7 Mg ha(-1)) as the respective values for AGB(Projected_2016) were 7.0% (9.9 Mg ha(-1)) and 21.5% (30.8 Mg ha(-1)). AGB(2016_pred2014) had a bias of -19.6% (-27.7 Mg ha(-1)) and RMSE of 33.2% (46.9 Mg ha(-1)). By combining predictions of AGB(2016) and AGB(Projected_2016) at sample plot level as a weighted average, we were able to decrease the bias notably compared to estimates made on any single date. The lowest bias of -0.25% (-0.4 Mg ha(-1)) was obtained when equal weights of 0.5 were given to the AGB(Projected_2016) and AGB(2016) estimates. Respectively, RMSE of 20.9% (29.5 Mg ha(-1)) was obtained using equal weights. Thus, we conclude that combination of two date WorldView-2 stereo-imagery improved the reliability of AGB estimates on sample plots where forest growth was the only change between the two dates. Peer reviewed

Forests are the most diverse terrestrial ecosystems and their biological diversity includes trees, but also other plants, animals, and micro-organisms. One-third of the forested land is in boreal zone; therefore, changes in biological diversity in boreal forests can shape biodiversity, even at global scale. Several forest attributes, including size variability, amount of dead wood, and tree species richness, can be applied in assessing biodiversity of a forest ecosystem. Remote sensing offers complimentary tool for traditional field measurements in mapping and monitoring forest biodiversity. Recent development of small unmanned aerial vehicles (UAVs) enable the detailed characterization of forest ecosystems through providing data with high spatial but also temporal resolution at reasonable costs. The objective here is to deepen the knowledge about assessment of plot-level biodiversity indicators in boreal forests with hyperspectral imagery and photogrammetric point clouds from a UAV. We applied individual tree crown approach (ITC) and semi-individual tree crown approach (semi-ITC) in estimating plot-level biodiversity indicators. Structural metrics from the photogrammetric point clouds were used together with either spectral features or vegetation indices derived from hyperspectral imagery. Biodiversity indicators like the amount of dead wood and species richness were mainly underestimated with UAV-based hyperspectral imagery and photogrammetric point clouds. Indicators of structural variability (i.e., standard deviation in diameter-at-breast height and tree height) were the most accurately estimated biodiversity indicators with relative RMSE between 24.4% and 29.3% with semi-ITC. The largest relative errors occurred for predicting deciduous trees (especially aspen and alder), partly due to their small amount within the study area. Thus, especially the structural diversity was reliably predicted by integrating the three-dimensional and spectral datasets of UAV-based point clouds and hyperspectral imaging, and can therefore be further utilized in ecological studies, such as biodiversity monitoring. Peer reviewed

Unmanned airborne vehicles (UAV) equipped with novel, miniaturized, 2D frame format hyper- and multispectral cameras make it possible to conduct remote sensing measurements cost-efficiently, with greater accuracy and detail. In the mapping process, the area of interest is covered by multiple, overlapping, small-format 2D images, which provide redundant information about the object. Radiometric correction of spectral image data is important for eliminating any external disturbance from the captured data. Corrections should include sensor, atmosphere and view/illumination geometry (bidirectional reflectance distribution function—BRDF) related disturbances. An additional complication is that UAV remote sensing campaigns are often carried out under difficult conditions, with varying illumination conditions and cloudiness. We have developed a global optimization approach for the radiometric correction of UAV image blocks, a radiometric block adjustment. The objective of this study was to implement and assess a combined adjustment approach, including comprehensive consideration of weighting of various observations. An empirical study was carried out using imagery captured using a hyperspectral 2D frame format camera of winter wheat crops. The dataset included four separate flights captured during a 2.5 h time period under sunny weather conditions. As outputs, we calculated orthophoto mosaics using the most nadir images and sampled multiple-view hyperspectral spectra for vegetation sample points utilizing multiple images in the dataset. The method provided an automated tool for radiometric correction, compensating for efficiently radiometric disturbances in the images. The global homogeneity factor improved from 12–16% to 4–6% with the corrections, and a reduction in disturbances could be observed in the spectra of the object points sampled from multiple overlapping images. Residuals in the grey and white reflectance panels were less than 5% of the reflectance for most of the spectral bands.

Academy of Finland Academy of Finland: 273806 The objective of this investigation was to study and optimize a hyperspectral unmanned aerial vehicle (UAV)-based remote-sensing system for the Brazilian environment. Comprised mainly of forest and sugarcane, the study area was located in the western region of the State of São Paulo. A novel hyperspectral camera based on a tunable Fabry–Pérot interferometer was mounted aboard a UAV due to its flexibility and capability to acquire data with a high temporal and spatial resolution. Five approaches designed to produce mosaics of hyperspectral images, which represent the hemispherical directional reflectance factor of targets in the Brazilian environment, are presented and evaluated. The method considers the irradiance variation during image acquisition and the effects of the bidirectional reflectance distribution function. The main goal was achieved by comparing the spectral responses of radiometric reference targets acquired with a spectroradiometer in the field with those produced by the five different approaches. The best results were achieved by correcting the bidirectional reflectance distribution function effects and by applying a least squares method to a radiometric block adjustment using only the image data, thereby achieving a root mean square error of 11.35%.

In this paper, we demonstrate the feasibility of using the Microsoft Kinect and Google Tango frame-based depth sensors for individual tree stem measurements and reconstruction for the purpose of forest inventory. Conventionally field reference data in forest inventory are collected at tree and sample plot level by means of manual measurements (e.g., a caliper), which are both labor-intensive and time-consuming. In this study, color (i.e., red, green and blue channels, RGB) and range images acquired by a Kinect and Tango systems were processed and used to extract tree diameter measurements for the individual tree stems. For this, 121 reference stem diameter measurements were made with tape and caliper. Kinect-derived tree diameters agreed with tape measurements to a 1.90 cm root-mean-square error (RMSE). The stem curve from the ground to the diameter at breast height agreed with a bias of 0.7 cm and random error of 0.8 cm with respect to the reference trunk. For Tango measurements, the obtained stem diameters matched those from tape measurement with an RMSE of 0.73 cm, having an average bias of 0.3 cm. As highly portable and inexpensive systems, both Kinect and Tango provide an easy way to collect tree stem diameter and stem curve information vital to forest inventory. These inexpensive instruments may in future compete with both terrestrial and mobile laser scanning or conventional fieldwork using calipers or tape. Accuracy is adequate for practical applications in forestry. Measurements made using Kinect and Tango type systems could also be applied in crowdsourcing context. Peer reviewed