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Phytosanitary control of crops requires the rapid mapping of diseases to enable management attention. This study aimed to evaluate the potential of vegetation indices for the detection of sugarcane mosaic disease. Spectral indices were applied to hyperspectral images collected by an unmanned aerial vehicle (UAV) to find the areas affected by the mosaic virus in sugarcane. Identifying indices capable of detecting diseased plants in agricultural crops supports data processing and the development of efficient tools. A new index was designed based on spectral regions, which presents higher differences between healthy and mosaic virus-infected leaves to enhance hyperspectral image pixels representing diseased plants. Based on the data generated, we propose the anthocyanin red edge index (AREI) for mosaic virus detection in sugarcane plantations. An index that can adequately identify sugarcane infected by the mosaic virus may incorporate wavelengths associated with variations in leaf pigment concentrations as well as changes in leaf structure. The indices that assessed to detect plants infected with the sugarcane mosaic virus were the normalised difference vegetation index (NDVI), normalised difference vegetation index red edge (NDVI705), new vegetation index (NVI), ARI2 and AREI. The results showed that AREI presented the best performance for the detection of mosaic in sugarcane from UAV images, giving an overall accuracy of 0.94, a kappa coefficient of 0.87, and omission and inclusion errors of 2.86% and 10.52%, respectively. The results show the importance of wavelengths associated with the concentration of chlorophyll and anthocyanin and the position of the red edge for the detection of diseases in sugarcane.

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.

Structural complexity of trees is related to various ecological processes and ecosystem services. To support management for complexity, there is a need to assess the level of structural complexity objectively. The fractal-based box dimension (Db) provides a holistic measure of the structural complexity of individual trees. This study aimed to compare the structural complexity of Scots pine (Pinus sylvestris L.) trees assessed with Db that was generated with point cloud data from terrestrial laser scanning (TLS) and aerial imagery acquired with an unmanned aerial vehicle (UAV). UAV imagery was converted into point clouds with structure from motion (SfM) and dense matching techniques. TLS and UAV measured Db-values were found to differ from each other significantly (TLS: 1.51 ± 0.11, UAV: 1.59 ± 0.15). UAV measured Db-values were 5% higher, and the range was wider (TLS: 0.81–1.81, UAV: 0.23–1.88). The divergence between TLS and UAV measurements was found to be explained by the differences in the number and distribution of the points and the differences in the estimated tree heights and number of boxes in the Db-method. The average point density was 15 times higher with TLS than with UAV (TLS: 494,000, UAV 32,000 points/tree), and TLS received more points below the midpoint of tree heights (65% below, 35% above), while UAV did the opposite (22% below, 78% above). Compared to the field measurements, UAV underestimated tree heights more than TLS (TLS: 34 cm, UAV: 54 cm), resulting in more boxes of Db-method being needed (4–64%, depending on the box size). Forest structure (two thinning intensities, three thinning types, and a control group) significantly affected the variation of both TLS and UAV measured Db-values. Still, the divergence between the two approaches remained in all treatments. However, TLS and UAV measured Db-values were consistent, and the correlation between them was 75%.

The Atlantic Forest is the most fragmented and threatened domain in Brazil. The main remnants are in the coastal regions. This paper describes a study performed at a protected federal reserve in Brazil located in western of São Paulo state, which is a transition with the Savannah. A forestry survey was made for understanding the forest structure, diversity, and floristic composition of an inland Atlantic Forest area. A total of 3,181 individuals with a Diameter at Breast Height over 3.5 cm were sampled. The data sample was composed of 29 families and 64 species from 15 plots. Forty-seven percent of the species were classified as a pioneer, 42% as secondary, and 11% as climax. The species Eugenia uniflora presented the highest importance value index. The values of Shannon-Weaver diversity and Pielou equitability index indicate the area has less diversity than others in the same phytophysiognomy and was dominated by a few species with many individuals. Several anthropogenic disturbances altered the forest cover of the Ponte Branca Forest remnant, which is in the process of secondary succession.

It can be concluded that close-range remote sensing has fundamentally changed the landscape of forest in situ inventories. The most significant impact is that the technology has turned many previously impossible investigation scenarios into possible ones, reshaping the possibilities for future forest in situ observations by improving the automation, detail, accuracy, and comprehensiveness of data collection. The urgent problems to solve include the limited completeness and geometric accuracy of data as well as insufficient processing power, which limit advanced and practical applications and call for further studies. This review also provides practitioners useful guidance to select suitable systems and operational protocols when applying close-range remote sensing to collect tree and forest attributes at individual tree and plot levels.

The deterioration of road conditions and increasing repair deficits pose challenges for the maintenance of reliable road infrastructure, and thus threaten, for example, safety and the fluent flow of traffic. Improved and more efficient procedures for maintenance are required, and these require improved knowledge of road conditions, i.e., improved data. Three-dimensional mapping presents possibilities for large-scale collection of data on road surfaces and automatic evaluation of maintenance needs. However, the development and, specifically, evaluation of large-scale mobile methods requires reliable references. To evaluate possibilities for close-range, static, high-resolution, three-dimensional measurement of road surfaces for reference use, three measurement methods and five instrumentations are investigated: terrestrial laser scanning (TLS, Leica RTC360), photogrammetry using high-resolution professional-grade cameras (Nikon D800 and D810E), photogrammetry using an industrial camera (FLIR Grasshopper GS3-U3-120S6C-C), and structured-light handheld scanners Artec Leo and Faro Freestyle. High-resolution photogrammetry is established as reference based on laboratory measurements and point density. The instrumentations are compared against one another using cross-sections, point–point distances, and ability to obtain key metrics of defects, and a qualitative assessment of the processing procedures for each is carried out. It is found that photogrammetric models provide the highest resolutions (10–50 million points per m2) and photogrammetric and TLS approaches perform robustly in precision with consistent sub-millimeter offsets relative to one another, while handheld scanners perform relatively inconsistently. A discussion on the practical implications of using each of the examined instrumentations is presented.

Made available in DSpace on 2022-04-29T08:38:21Z (GMT). No. of bitstreams: 0 Previous issue date: 2021-11-01 National Research Council Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) The vegetative growth of forest canopies changes their spectral response, which can be detected by multispectral sensors and enhanced by utilizing the normalized difference vegetation index (NDVI). The structural variability of canopies in heterogeneous forests can also be related to successional stages. Thereby, a spatiotemporal methodology is presented to associate the 3D photogrammetric information, derived from aerial images, with the NDVI time series extracted from the Landsat imagery, using spatial units connected to ecological succession. The technique is based on time series clustering within superpixels (extracted from the local variance of tree heights) and on trend analysis of the canopy using the breaks for additive season and trend (BFAST) algorithm. The study was conducted in a tropical native forest remnant (Inland Atlantic Forest) in the western region of São Paulo State, Brazil. We investigated the relationship between the variability of the forest vertical structure and the NDVI temporal trajectory associated with vegetation vigor in the period from 1984 to 2010. The experiments produced a regularized time series of overlaid superpixels. The cluster statistical analysis was successful in separating the NDVI trajectory into ten classes associated to successional stages, in which the evolution of the vegetation vigor could be observed and compared within degraded, transitional, and preserved areas. Such areas overlapping with the ten trajectory classes allowed us to quantify that 59.9% of the clusters were related to preserved areas, 30.1% to transitional areas, and 10.0% to degraded areas. Additionally, the BFAST algorithm allowed the identification of trend measures and the occurrence of disturbance events within these trajectory classes based on the vegetation response. Therefore, the classification of NDVI trajectory clusters helps to understand how the areas in a heterogeneous forest with different succession stages develop spectrally. Furthermore, monitoring secondary forests is essential for the conservation of biodiversity, ecosystem functioning, and carbon stock, among other values. Department of Cartography UNESP - Universidade Estadual Paulista, Rua Roberto Simonsen 305 Department of Remote Sensing and Photogrammetry Finnish Geospatial Research Institute in National Land Survey of Finland, Geodeetinrinne 2 Department of Cartography UNESP - Universidade Estadual Paulista, Rua Roberto Simonsen 305 National Research Council: 150306/2018–0 FAPESP: 2013/50426–4 FAPESP: 2014/05033–7

Solar-induced Fluorescence (SIF) has an advantage over greenness-based Vegetation Indices in detecting drought. This advantage is the mechanistic coupling between SIF and Gross Primary Productivity (GPP). Under water stress, SIF tends to decrease with photosynthesis, due to an increase in non-photochemical quenching (NPQ), resulting in rapid and/or sustained reductions in the fluorescence quantum efficiency (phi F). Water stress also affects vegetation structure via highly dynamic changes in leaf angular distributions (LAD) or slower changes in leaf area index (LAI). Critically, these responses are entangled in space and time and their relative contribution to SIF, or to the coupling between SIF and GPP, is unclear. In this study, we quantify the relative effect of structural and photosynthetic dynamics on the diurnal and spatial variation of canopy SIF in a potato crop in response to a replicated paired-plot water stress experiment. We measured SIF using two platforms: a hydraulic lift and an Unmanned Aerial Vehicle (UAV) to capture temporal and spatial variation, respectively. LAD parameters were estimated from point clouds and photographic data and used to assess structural dynamics. Leaf phi F estimated from PAM fluorescence measurements were used to represent variations in photosynthetic regulation. We also measured foliar pigments, operating quantum yield of photosystem II (PSII), photosynthetic gas exchange, stomatal conductance and LAI. We used a radiative transfer model (SCOPE) to provide a means of decoupling structural and photosynthetic factors across the diurnal and spatial domains. The results demonstrate that diurnal variation in SIF is driven by photosynthetic and structural dynamics. The influence of phi F was prominent in the diurnal SIF response to water stress, with reduced fluorescence efficiencies in stressed plants. Structural factors dominated the spatial response of SIF to water stress over and above phi F. The results showed that the relationship between SIF and GPP is maintained in response to water stress where adjustments in NPQ and leaf angle co-operate to enhance the correlation between SIF and GPP. This study points to the complexity of interpreting and modelling the spatiotemporal connection between SIF and GPP which requires simultaneous knowledge of vegetation structural and photosynthetic dynamics.

Made available in DSpace on 2021-06-25T10:56:06Z (GMT). No. of bitstreams: 0 Previous issue date: 2021-07-01 Canopy height is an important attribute that allows characterizing the forest vertical structure and analyze changes in vegetation cover over time. The objective of this study is to develop an approach for a spatio-temporal analysis of the tropical forest canopy using multi-temporal photogrammetric images. The datasets based on film and digital cameras are used to generate canopy height models and extract structural variables (tree height, relative variance between tree heights, and density of higher trees in the upper canopy). The combination of these variables is used in the analysis. Each variable is segmented into ordinal categorical classes in its respective dataset with temporal class gradients being obtained between the periods of the multi-temporal datasets. Experiments were conducted in a tropical forest under regeneration and with diversity of tree species in different successional stages. Three sets of images (years 1978, 2010, and 2017) were used for analyzing canopy cover changes. A classification based on histograms of gradient classes indicated and quantified the most frequent behavior of the canopy over time. The results showed that the most significant variations in cover changes could be explained by 13 classes of temporal gradients, which described 88% of the canopy. This classification was validated with field data collected in sample plots. From the results, it can be concluded that the proposed approach provides accurate assessments of the spatio-temporal canopy cover changes for forest management. Department of Cartography UNESP - Universidade Estadual Paulista, Rua Roberto Simonsen 305 Department of Remote Sensing and Photogrammetry Finnish Geodetic Institute FGI National Land Survey of Finland Department of Cartography UNESP - Universidade Estadual Paulista, Rua Roberto Simonsen 305

Made available in DSpace on 2022-04-29T08:30:19Z (GMT). No. of bitstreams: 0 Previous issue date: 2021-07-01 Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) Data collection and estimation of variables that describe the structure of tropical forests, diversity, and richness of tree species are challenging tasks. Light detection and ranging (LiDAR) is a powerful technique due to its ability to penetrate small openings and cracks in the forest canopy, enabling the collection of structural information in complex forests. Our objective was to identify the most significant LiDAR metrics and machine learning techniques to estimate the stand and diversity variables in a disturbed heterogeneous tropical forest. Data were collected in a remnant of the Brazilian Atlantic Forest with different successional stages. LiDAR metrics were used in three types of transformation: (i) raw data (untransformed), (ii) correlation analysis, and (iii) principal component analysis (PCA). These transformations were tested with four machine learning techniques: (i) artificial neural network (ANN), ordinary least squares (OLS), random forests (RF), and support vector machine (SVM) with different configurations resulting in 27 combinations. The best technique was determined based on the lowest RMSE (%) and corrected Akaike information criterion (AICc), and bias (%) values close to zero. The output forest variables were mean diameter at breast height (MDBH), quadratic mean diameter (QMD), basal area (BA), density (DEN), number of tree species (NTS), as well as Shannon–Waver (H’) and Simpson’s diversity indices (D). The best input data were the new variables obtained from the PCA, and the best modeling method was ANN with two hidden layers for the variables MDBH, QMD, BA, and DEN while for NTS, H’and D, the ANN with three hidden layers were the best methods. For MDBH, QMD, H’and D, the RMSE was 5.2–10% with a bias between −1.7% and 3.6%. The BA, DEN, and NTS were the most difficult variables to estimate, due to their complexity in tropical forests; the RMSE was 16.2–27.6% and the bias between −12.4% and −0.24%. The results showed that it is possible to estimate the stand and diversity variables in heterogeneous forests with LiDAR data. São Paulo State University (UNESP), Roberto Simonsen 305 Department of Cartography São Paulo State University (UNESP), Roberto Simonsen 305 Department of Forestry Federal Rural University of Amazonia (UFRA), Tv. Pau Amarelo s/n ERATOSTHENES Centre of Excellence Laboratory of Remote Sensing and Geo-Environment Department of Civil Engineering and Geomatics School of Engineering and Technology Cyprus University of Technology Finnish Geospatial Research Institute (FGI) National Land Survey of Finland, Geodeetinrinne 2 São Paulo State University (UNESP), Roberto Simonsen 305 Department of Cartography São Paulo State University (UNESP), Roberto Simonsen 305 FAPESP: 2013/50426-4

Brazil is one of the world leaders in citrus plantation, and the production of orange juice is economically important in the export scenario, being regarded as a fundamental agricultural commodity in Brazil. The worst citrus disease is Greening, or Huanglongbing (HLB), a bacterial disease which cannot be cured and to which no plant variety is immune or resistant. Currently, control of HLB is through the inspection of orchards and the immediate elimination of plants displaying HLB symptoms, plus chemical or biological control of the insect vector (psyllid). The HLB disease has high economic impact on Brazilian and world citriculture, due to the extreme damage to crops. Based on remote sensing techniques, the mapping of diseased and healthy citrus plants from hyperspectral images was carried out, generating a product that could help in the monitoring and control of HLB in Brazilian citrus orchards. The methodology to produce a health map goes through the following stages: aerial image acquisition, radiometric field measurements, hyperspectral cube orientation, and data analysis for detection of HLB in citrus. The field survey was performed in Guacho Farm- Brazil and hyperspectral images were acquired by a Rikola camera onboard a light aircraft, obtaining images with 0.50 m of Ground Sample Distance (GSD). The hyperspectral cubes were classified with Spectral Angle Mapper (SAM) algorithm to produce the health map. Plants infected with HLB were detected with an accuracy 61.2%, the validation of the health map was verified by samples were analysed in the laboratory to confirm HLB.

The information on the grass quantity and quality is needed for several times in a growing season for making optimal decisions about the harvesting time and the fertiliser rate, especially in northern countries, where grass swards quality declines and yield increases rapidly in the primary growth. We studied the potential of UAV-based photogrammetry and spectral imaging in grass quality and quantity estimation. To study this, a trial site with large variation in the quantity and quality parameters was established by using different nitrogen fertilizer application rates and harvesting dates. UAV-based remote sensing datasets were captured four times during the primary growth season in June 2017 and agricultural reference measurements including dry biomass and quality parameters, such as the digestibility (D-value) were collected simultaneously. The datasets were captured using a flying height of 50 m which provided a GSD of 0.7 cm for the photogrammetric imagery and 5 cm for the hyperspectral imagery. A rigorous photogrammetric workflow was carried out for all data sets aiming to determine the image exterior orientation parameters, camera interior orientation parameters, 3D point clouds and orthomosaics. The quantitative radiometric calibration included sensor corrections, atmospheric correction, and correction for the radiometric non-uniformities caused by illumination variations, BRDF correction and the absolute reflectance transformation. Random forest (RF) and multilinear regression (MLR) estimators were trained using spectral bands, vegetation indices and 3D features, extracted from the remote sensing datasets, and insitu reference measurements. From the FPI hyperspectral data, the 35 spectral bands and 11 spectral indices were used. The 3D features were extracted from the canopy height model (CHM) generated using RGB data. The most accurate results were obtained in the second measurement day (15th June) which was near to the optimal harvesting time and generally RF outperformed MLR slightly. When assessed with the leave-one-out-estimation, the best root mean squared error (RMSE%) were 8.9% for the dry biomass using 3D features. The best D-value estimation using RF algorithm (RMSE% = 0.87%) was obtained using spectral features. Using the estimators, we then calculated grass quality and quantity maps covering the entire test site to compare different techniques and to evaluate the variability in the field. The results showed that the low-cost drone remote sensing gave excellent precision both for biomass and quality parameter estimation if accurately calibrated, offering an excellent tool for efficient and accurate management of silage grass production.

A study on orientation of hyperspectral band cubes acquired with frame camera is presented in this paper. The camera technology is based on a tuneable Fabry-Perot Interferometer (FPI) and captures cubes of images sequentially using two sensors. However, the bands are not recorded at the same instant, which results different exterior orientation parameters (EOPs) for each image band. A technique based on polynomial model is assessed, which determines the EOPs within the hypercube from few sample bands, since a large number of bands are generated. Experiments were performed to assess the feasibility of using the polynomial technique. An analysis of the UAV trajectory was performed and the results of the polynomial technique were compared with those obtained by a conventional bundle adjustment. The trials showed that the results of both techniques were comparable, indicating that the time-dependent polynomial model can be used to estimate the EOPs of all spectral bands, without requiring a bundle adjustment including all bands. The accuracy of the block adjustment was analysed based on the discrepancies obtained from independent checkpoints. The root mean square error (RMSE) was calculated and showed an accuracy of approximately 1 GSD in planimetry and 1.5 GSD in altimetry. This accurate result is important because the proposed technique can significantly reduce the processing workload.

Made available in DSpace on 2019-10-06T15:26:36Z (GMT). No. of bitstreams: 0 Previous issue date: 2019-01-01 Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) Academy of Finland Miniaturised 2D frame format hyperspectral camera technology that is suitable for small unmanned aerial vehicles (UAVs) has entered the market, making the generation of hyperspectral digital surface models (HDSMs) feasible. HDSMs offer a rigorous approach to capturing the target spectral and 3D geometric data. The main objective of this investigation was to study and develop techniques for the generation of HDSMs in forest areas using novel hyperspectral 2D frame camera technologies. An approach based on object-space image matching was developed, adapting the traditional vertical line locus (VLL) method for HDSM generation; this was then named the hyperspectral VLL (HVLL) approach. Additionally, image classification was introduced into the processing chain in order to adapt the matching parameters, based on different classes. We also proposed a method for extracting the spectral and viewing angle information of the points. An empirical study was carried out using UAV datasets from tropical and boreal forests using 2D format hyperspectral cameras, based on tuneable Fabry-Pérot interferometer (FPI) technology. Quality assessment was performed using DSMs based on state-of-the-art commercial software and airborne laser scanning (ALS). The results showed that the proposed technique generated a high-quality HDSM in both tested environments. The HDSM had higher deviations over the continuous canopy cover than the digital surface models (DSMs) generated using commercial software. The method using image classification information outperformed the commercial approach with respect to the ability to measure ground points in shadowed areas and in canopy gaps. The proposed method is of great interest in supporting automated interpretations of novel multi- and hyperspectral imaging technologies, especially when applied complex objects, such as forests. UNESP São Paulo State University Finnish Geospatial Research Institute, Masala, Kirkkonummi UNESP São Paulo State University FAPESP: 2013/17787-3 FAPESP: 2013/50426-4 FAPESP: 2014/24844-6 Academy of Finland: 273806

Made available in DSpace on 2019-10-06T16:05:02Z (GMT). No. of bitstreams: 0 Previous issue date: 2018-12-01 Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) Academy of Finland Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) Airborne photogrammetric image archives offer interesting possibilities for multi-temporal analyses of environmental evolution. The objective of this investigation was to develop a technique for classifying forest successional stages and performing multi-temporal analyses of the tree canopy based on tree height variances calculated from digital surface models (DSMs) created from photogrammetric imagery. Furthermore, our objective was to evaluate the usability of the technique in assessing the evolution of successional stages in a tropical forest. The local variance calculation in 3D space resulted in an image that was subdivided with a segmentation technique to generate small areas called superpixels. These superpixels, which use the local mean variance as an attribute, are assessed via cluster analysis to evaluate statistical similarity and define successional stage classes. The same superpixel shapes were located in georeferenced historical datasets to enable multi-temporal analysis. The cluster analysis of temporal superpixels enabled the spatiotemporal classification of forest canopy evolution. The technique was used to assess a tropical forest remnant in Brazil. Dense DSMs were generated with stereo-photogrammetric techniques using optical images (both film and digital images) from which height variances were computed. A cluster analysis of superpixels was performed to classify the forest canopy into four successional stages, which were consistent with Brazilian classification rules. The multi-temporal analysis identified six classes of forest cover evolution. Field data were collected in forest plots to validate the generated forest canopy classifications. The results showed that the proposed approach was feasible for forest cover classification and for identifying changes in the vertical forest structure and cover over time using only optical images. Department of Cartography UNESP – São Paulo State University, Rua Roberto Simonsen 305 Department of Geography UNESP – São Paulo State University, Rua Roberto Simonsen 305 Department of Remote Sensing and Photogrammetry Finnish Geospatial Research Institute FGI National Land Survey of Finland Department of Cartography UNESP – São Paulo State University, Rua Roberto Simonsen 305 Department of Geography UNESP – São Paulo State University, Rua Roberto Simonsen 305 FAPESP: 2013/50426-4 FAPESP: 2014/05033-7 Academy of Finland: 273806 CNPq: 305111/2010-8

Simultaneous localization and mapping (SLAM) of a monocular projective camera installed on an unmanned aerial vehicle (UAV) is a challenging task in photogrammetry, computer vision, and robotics. This paper presents a novel real-time monocular SLAM solution for UAV applications. It is based on two steps: consecutive construction of the UAV path, and adjacent strip connection. Consecutive construction rapidly estimates the UAV path by sequentially connecting incoming images to a network of connected images. A multilevel pyramid matching is proposed for this step that contains a sub-window matching using high-resolution images. The sub-window matching increases the frequency of tie points by propagating locations of matched sub-windows that leads to a list of high-frequency tie points while keeping the execution time relatively low. A sparse bundle block adjustment (BBA) is employed to optimize the initial path by considering nuisance parameters. System calibration parameters with respect to global navigation satellite system (GNSS) and inertial navigation system (INS) are optionally considered in the BBA model for direct georeferencing. Ground control points and checkpoints are optionally included in the model for georeferencing and quality control. Adjacent strip connection is enabled by an overlap analysis to further improve connectivity of local networks. A novel angular parametrization based on spherical rotation coordinate system is presented to address the gimbal lock singularity of BBA. Our results suggest that the proposed scheme is a precise real-time monocular SLAM solution for a UAV.

There are many advantages of using unmanned aerial vehicles (UAVs) in remote sensing but when using radiometrically corrected multispectral images. This study focuses on two techniques of obtain a multispectral orthomosaic with suitable radiometric quality considering a day period with minor variations in illumination and clouds. The first technique comprises a radiometric block adjustment combined with empirical line whilst the second technique uses only empirical line. Field measurements with spectrometers were used to assess the techniques. The obtained results show that the radiometric block adjustment presented better results when compared to the radiometric reference targets and its calculated Hemispherical Conical Reflectance Factor (HCRF) from the spectrometer. However, the root mean square error (RMSE), normalized root mean square error (NRMSE) and mean absolute percentage error (MAPE) were similar in both cases, showing that the two proposed workflows can generate multispectral mosaics with acceptable radiometric quality for a period in which illumination conditions are stable. Images difference between each band was produced showing that there was a stronger variation of pixels in the higher slope region, which indicates that additional corrections beyond empirical line are needed in these situations

The use of remote sensing in agriculture is expanding due to innovation in sensors and platforms. Drones, high resolution instruments on CubeSats, and robot mounted proximal phenotyping sensors all feature in this drive. Common threads include a focus on high spatial and spectral resolution coupled with the use of machine learning methods for relating observations to crop parameters. As the best-known vegetation index, the Normalized Difference Vegetation Index (NDVI), which quantifies the difference in canopy scattering in the near-infrared and photosynthetic light absorption in the red, is spearheading this drive. Importantly, there are decades of research on the physical principals of the NDVI, relating to soil, structural and measurement geometry effects. Here we bridge the gap between the historical research, grounded in physically based theory, and the recent field-based developments, to ask the question: What does field sensed NDVI tell us about crops? We answer this question with data from two crop field sites featuring field mounted spectral reflectance sensors and a drone-based spectroscopy system. The results show how ecosystem processes can be followed using the NDVI, but also how crop structure and soil reflectance controls data collected in wavelength space.

Terrestrial laser scanning (TLS) provides a detailed three-dimensional representation of surrounding forest structures. However, due to close-range hemispherical scanning geometry, the ability of TLS technique to comprehensively characterize all trees, and especially upper parts of forest canopy, is often limited. In this study, we investigated how much forest characterization capacity can be improved in managed Scots pine (Pinus sylvestris L.) stands if TLS point clouds are complemented with photogrammetric point clouds acquired from above the canopy using unmanned aerial vehicle (UAV). In this multisensorial (TLS+UAV) close-range sensing approach, the used UAV point cloud data were considered especially suitable for characterizing the vertical forest structure and improvements were obtained in estimation accuracy of tree height as well as plot-level basal-area weighted mean height (Hg) and mean stem volume (Vmean). Most notably, the root-mean-square-error (RMSE) in Hg improved from 0.8 to 0.58 m and the bias improved from -0.75 to -0.45 m with the multisensorial close-range sensing approach. However, in managed Scots pine stands, the mere TLS also captured the upper parts of the forest canopy rather well. Both approaches were capable of deriving stem number, basal area, Vmean, Hg, and basal area-weighted mean diameter with the relative RMSE less than 5.5% for all the sample plots. Although the multisensorial close-range sensing approach mainly enhanced the characterization of the forest vertical structure in single-species, single-layer forest conditions, representation of more complex forest structures may benefit more from point clouds collected with sensors of different measurement geometries.

Wind damage is known for causing threats to sustainable forest management and yield value in boreal forests. Information about wind damage risk can aid forest managers in understanding and possibly mitigating damage impacts especially when wind damage events have increased in recent years.The objective of this research was to better understand and quantify drivers of wind damage, and to map the probability of wind damage and to provide information that could be used to support decision making in forest management planning, as well as in other sectors (e.g. electricity companies). To accomplish this, we used open-access airborne scanning light detection and ranging (LiDAR) data. LiDAR data can provide wall-to-wall coverage and are best suited for monitoring of the dominant trees. In addition multitemporal LiDAR is highly capable of monitoring abiotic tree or stand level changes. The LiDAR data used are openly accessible for public from NLS and are mainly used for generating digital terrain model (DTM). Potential drivers associated with the probability of wind-induced forest damage were examined using a multivariate logistic regression model which was well suited to the discrete nature of the dependent variable (i.e., damage, no damage) and it has been used widely in the modelling of forest disturbances. Risk model predictors related to topography and vegetation height were extracted from the LiDAR-derived surface models such as DTM and canopy height model (CHM). The strongest predictors in the risk model were mean canopy height and mean elevation. Damaged sample grid cells covered 45,6% of the entire sample and they were mainly dominated by Norway spruce. CHM mean and maximum were higher in damaged sample cells which can be expected to correlate with the result where mean volume was also larger in damaged sample cells than in undamaged. Regression model output was a continuous probability surface whereby the probability for wind damage is interpreted as risk (e.g. areas with high probability of wind damage can be described as high risk areas). With increasing frequency of wind damage events, there is a need to identify areas of high wind damage risk. The selected predictor variables, mean elevation describing local topography and mean canopy height, can provide valuable information on the damage probability (i.e. risk) in a robust way.

Terrestrial laser scanning (TLS) provides detailed three-dimensional representation of the surrounding forest structure. However, due to close-range hemispherical scanning geometry, the ability of TLS technique to comprehensively characterize all trees and especially the upper parts of forest canopy is often limited. In this study, we investigated how much forest characterization capacity can be improved in managed Scots pine (Pinus sylvestris L.) stands if TLS point cloud is complemented with a photogrammetric point cloud acquired from above the canopy using unmanned aerial vehicle (UAV). In this multisensorial (TLS+UAV) close-range sensing approach, the used UAV point cloud data was considered feasible especially in characterizing the vertical forest structure and improvements were obtained in estimation accuracy of tree height as well as plot-level basal-area weighted mean height (Hg) and mean stem volume (Vmean). Most notably the root mean square error (RMSE) in Hg improved from 0.88 m to 0.58 m and the bias improved from -0.75 m to -0.45 m with the multisensorial close-range sensing approach. However, in managed Scots pine stands the mere TLS captured also the upper parts of the forest canopy rather well. Both approaches were capable of deriving stem number, basal area, Vmean, Hg and basal area-weighted mean diameter with a relative RMSE less than 5.5% for all of the sample plots. Although the multisensorial close-range sensing approach mainly enhanced characterization of forest vertical structure in single-species, single-layer forest conditions, representation of more complex forest structures may benefit more from point clouds collected with sensors of different measurement geometries.

National Land Survey of Finland (NLS) started collecting ALS-data in 2008 to provide a new national elevation model. The data is available at free of charge and has great potential and a wide variety of possible applications in spatial modelling. The objective of this study was to test the feasibility of ALS-data from NLS in mapping the risk of forest wind damage. The strongest predictors in the risk model were the mean height of vegetation, canopy cover and elevation. Risk model based on ALS data provided a good agreement with detected damaged areas.

Quantitative assessment of the effects of forest management on tree size and shape has been challenging as there has been a lack of methodologies for characterizing differences and possible changes comprehensively in space and time. Terrestrial laser scanning (TLS) and photogrammetric point clouds provide three-dimensional (3D) information on tree stem reconstructions required for characterizing differences between stem shapes and growth allocation. This data set includes 3D reconstructions of stems of Scots pine (Pinus sylvestris L.) trees from sample plots with different thinning treatments. The thinning treatments include two intensities of thinning, three thinning types as well as control (i.e. no thinning treatment since the establishment). The data set can be used in developing point clouds processing algorithms for single tree stem reconstruction and for investigating variation in stem size and shape of Scots pine trees. Additionally, it offers possibilities in characterizing the effects of various thinning treatments on stem size and shape of Scots pine trees from boreal forests.Data setZenodo https://zenodo.org/record/3701271Data set licenseAttribution 4.0 International (CC BY 4.0)

Terrestrial laser scanning (TLS) provides detailed three-dimensional representation of the surrounding forest structure. However, due to close-range hemispherical scanning geometry the ability of TLS technique to comprehensively characterize the upper parts of forest canopy is often limited. To overcome challenges in upper canopy characterization, TLS point cloud were complemented with a point cloud acquired from above the canopy using UAV photogrammetry. The use UAV point cloud data was considered feasible especially in tree segmentation. With multi-sensoral approach 98.8% of all the 2102 Scots pine trees on the 27 sample plots were automatically detected. Root-mean-square-error (RMSE) in tree height estimates was 1.47 m (7.4%) with 0.33 m (1.7%) of underestimation. Plot-level forest inventory attributes were estimated with a relative RMSE less than 5.5% with the multi-sensoral approach. The results showed that in managed Scots pine forests the multi-scan TLS captures also the upper parts of the forest canopy and improvement in tree height measurement accuracy was obtained with the use of photogrammetric UAV point clouds. The RMSE in basal area-weighted mean height improved 34% (from 0.88 m to 0.58 m) and the bias improved 40% (from -0.75 m to -0.45 m) when UAV data was utilized. However, in this case the accuracy of TLS measurement was already high. In single-species, single-layer forest conditions, multi-sensoral approach generated benefits especially for forest height characterization. However, characterization of complex forest structures may benefit even more from point clouds that have been collected using sensors with different measurement geometries.

Forest management alters the growing conditions and thus further development of trees. However, quantitative assessment of forest management on tree growth has been demanding as methodologies for capturing changes comprehensively in space and time have been lacking. Terrestrial laser scanning (TLS) has shown to be capable of providing three-dimensional (3D) tree stem reconstructions required for revealing differences between stem shapes and sizes. In this study, we used 3D reconstructions of tree stems from TLS and an unmanned aerial vehicle (UAV) to investigate how varying thinning treatments and the following growth effects affected stem shape and size of Scots pine (Pinus sylvestris L.) trees. The results showed that intensive thinning resulted in more stem volume and therefore total biomass allocation and carbon uptake compared to the moderate thinning. Relationship between tree height and diameter at breast height (i.e. slenderness) varied between both thinning intensity and type (i.e. from below and above) indicating differing response to thinning and allocation of stem growth of Scots pine trees. Furthermore, intensive thinning, especially from below, produced less variation in relative stem attributes characterizing stem shape and size. Thus, it can be concluded that thinning intensity, type, and the following growth effects have an impact on post-thinning stem shape and size of Scots pine trees. Our study presented detailed measurements on post-thinning stem growth of Scots pines that have been laborious or impracticable before the emergence of detailed 3D technologies. Moreover, the stem reconstructions from TLS and UAV provided variety of attributes characterizing stem shape and size that have not traditionally been feasible to obtain. The study demonstrated that detailed 3D technologies, such as TLS and UAV, provide information that can be used to generate new knowledge for supporting forest management and silviculture as well as improving ecological understanding of boreal forests.

Interest in drone solutions in forestry applications is growing. Using drones, datasets can be captured flexibly and at high spatial and temporal resolutions when needed. In forestry applications, fundamental tasks include the detection of individual trees, tree species classification, biomass estimation, etc. Deep neural networks (DNN) have shown superior results when comparing with conventional machine learning methods such as multi-layer perceptron (MLP) in cases of huge input data. The objective of this research is to investigate 3D convolutional neural networks (3D-CNN) to classify three major tree species in a boreal forest: pine, spruce, and birch. The proposed 3D-CNN models were employed to classify tree species in a test site in Finland. The classifiers were trained with a dataset of 3039 manually labelled trees. Then the accuracies were assessed by employing independent datasets of 803 records. To find the most efficient set of feature combination, we compare the performances of 3D-CNN models trained with hyperspectral (HS) channels, Red-Green-Blue (RGB) channels, and canopy height model (CHM), separately and combined. It is demonstrated that the proposed 3D-CNN model with RGB and HS layers produces the highest classification accuracy. The producer accuracy of the best 3D-CNN classifier on the test dataset were 99.6%, 94.8%, and 97.4% for pines, spruces, and birches, respectively. The best 3D-CNN classifier produced ~5% better classification accuracy than the MLP with all layers. Our results suggest that the proposed method provides excellent classification results with acceptable performance metrics for HS datasets. Our results show that pine class was detectable in most layers. Spruce was most detectable in RGB data, while birch was most detectable in the HS layers. Furthermore, the RGB datasets provide acceptable results for many low-accuracy applications.

Miniaturized hyperspectral imaging techniques have developed rapidly in recent years and have become widely available for different applications. Combining calibrated hyperspectral imagery with inverse physically based reflectance models is an interesting approach for estimating chlorophyll concentrations that are good indicators of vegetation health. The objective of this study was to develop a novel approach for retrieving chlorophyll a and b values from remotely sensed data by inverting the stochastic model of leaf optical properties using a one-dimensional convolutional neural network. The inversion results and retrieved values are validated in two ways: A classical machine learning validation dataset and calculating chlorophyll maps from empirical remotely sensed hyperspectral data and comparing them to TCARI OSAVI , an index that has strong negative correlation with chlorophyll concentration. With the validation dataset, coefficients of determination ( R 2 ) of 0.97 were obtained for chlorophyll a and 0.95 for chlorophyll b. The chlorophyll maps correlate with the TCARI OSAVI map. The correlation coefficient (R) is −0.87 for chlorophyll a and −0.68 for chlorophyll b in selected plots. These results indicate that the approach is highly promising approach for estimating vegetation chlorophyll content.

Made available in DSpace on 2020-12-12T01:58:32Z (GMT). No. of bitstreams: 0 Previous issue date: 2020-01-01 Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) The monitoring of forest resources is crucial for their sustainable management, and tree species identification is one of the fundamental tasks in this process. Unmanned aerial vehicles (UAVs) and miniaturized lightweight sensors can rapidly provide accurate monitoring information. The objective of this study was to investigate the use of multitemporal, UAV-based hyperspectral imagery for tree species identification in the highly diverse Brazilian Atlantic forest. Datasets were captured over three years to identify eight different tree species. The study area comprised initial to medium successional stages of the Brazilian Atlantic forest. Images were acquired with a spatial resolution of 10 cm, and radiometric adjustment processing was performed to reduce the variations caused by different factors, such as the geometry of acquisition. The random forest classification method was applied in a region-based classification approach with leave-one-out cross-validation, followed by computing the area under the receiver operating characteristic (AUCROC) curve. When using each dataset alone, the influence of different weather behaviors on tree species identification was evident. When combining all datasets and minimizing illumination differences over each tree crown, the identification of three tree species was improved. These results show that UAV-based, hyperspectral, multitemporal remote sensing imagery is a promising tool for tree species identification in tropical forests. Graduate Program in Cartographic Sciences São Paulo State University (UNESP), Roberto Simonsen 305 Department of Cartography São Paulo State University (UNESP), Roberto Simonsen, 305 Finnish Geospatial Research Institute National Land Survey of Finland, Geodeetinrinne, 2 Graduate Program in Cartographic Sciences São Paulo State University (UNESP), Roberto Simonsen 305 Department of Cartography São Paulo State University (UNESP), Roberto Simonsen, 305 CNPq: 153854/2016-2

Various biotic and abiotic stresses are threatening forests. Modern remote sensing technologies provide powerful means for monitoring forest health, and provide a sustainable basis for forest management and protection. The objective of this study was to develop unmanned aerial vehicle (UAV) based spectral remote sensing technologies for tree health assessment, particularly, for detecting the European spruce bark beetle (Ips typographus L.) attacks. Our focus was to study the early detection of bark beetle attack, i.e. the “green attack” phase. This is a difficult remote sensing task as there does not exist distinct symptoms that can be observed by the human eye. A test site in a Norway spruce (Picea abies (L.) Karst.) dominated forest was established in Southern-Finland in summer 2019. It had an emergent bark beetle outbreak and it was also suffering from other stress factors, especially the root and butt rot (Heterobasidion annosum (Fr.) Bref. s. lato). Altogether seven multitemporal hyper- and multispectral UAV remote sensing datasets were captured from the area in August to October 2019. Firstly, we explored deterioration of tree health and development of spectral symptoms using a time series of UAV hyperspectral imagery. Secondly, we trained assessed a machine learning model for classification of spruce health into classes of “bark beetle green attack”, “root-rot”, and “healthy”. Finally, we demonstrated the use of the model in tree health mapping in a test area. Our preliminary results were promising and indicated that the green attack phase could be detected using the accurately calibrated spectral image data.

In near future it is assumable that automated unmanned aerial platforms are coming more common. There are visions that transportation of different goods would be done with large planes, which can handle over 1000 kg payloads. While these planes are used for transportation they could similarly be used for remote sensing applications by adding sensors to the planes. Hyperspectral imagers are one this kind of sensor types. There is need for the efficient methods to interpret hyperspectral data to the wanted water quality parameters. In this work we survey the performance of neural networks in the prediction of water quality parameters from remotely sensed hyperspectral data in freshwater basins. The hyperspectral data consists of 36 bands in the wavelength range of 508–878 nm and the water quality parameters to be predicted are temperature, conductivity, turbidity, Secchi depth, blue-green algae, chlorophyll-a, total phosphorus, acidity and dissolved oxygen. The objective of this investigation was to study the behaviour of different types of neural networks with this kind of data. Study is a survey of the operation of neural networks on this problem, which can be used as a basis for the design of a more comprehensive study. The neural network types examined were multilayer perceptron and 1-, 2- and 3-dimensional convolutional neural networks with the effect of scaling the hyperspectral data with standard or min-max -scaler recorded. We also investigated investigated how the prediction of individual water quality parameter depends on whether the neural network model is done solely with respect to this one parameter or with several parameters predicted simultaneously with the same model. The results of the correspondence between the predicted and measured water quality parameters were presented with normalized root mean square error, Pearson correlation coefficient and coefficient of determination. The best models were obtained the 2-dimensional convolutional neural networks with standard scaling made separately for each parameter. The parameters showing good predictability were conductivity, turbidity, Secchi-depth, blue-green algae, chlorophyll-a and total phosphorus, for which the coefficient of determination was at least 0.96 (apart from Secchi-depth even 0.98). peerReviewed

Leaf angle distribution (LAD) is a key canopy structural parameter, playing an important role in light transfer. LAD can be estimated from fixed point of view photography, however this is time consuming and spatially limited. Recently, Terrestrial LiDAR Scanning (TLS) has been used to estimate LAD through 3D canopy space. The downside of TLS it is more costly than the cameras used in the photographic method. We propose a cost effective method to estimate LAD from drone based photogrammetry. We compare LAD estimates in different water treatment plots. Results show that LAD can be obtained from photogrammetric point clouds. Leaf angles were enhanced in stressed plots, presumably due to wilting. Further, the leaf azimuth distribution was not random but concentrated around 0 and 180 degrees. In summary, drone based photogrammetry can be used to estimate remote sensing parameters such as LAD paving the way for cost effective trait estimation.

We study and analyse performance of a system for direct reflectance measurements from a drone. Key instruments of the system are upwards looking irradiance sensor and downwards looking imaging spectrometer. Requirement for both instruments is that they are radiometrically calibrated, the irradiance sensor has to be horizontally stabilized, and the sensors needs to be accurately synchronized. In our system, irradiance measurements are done with FGI Aerial Image Reference System (FGI AIRS), which uses novel optical levelling methodology and can compensate sensor tilting up to 15°. We performed SI-traceable spectral and radiance calibration of FPI hyperspectral camera at the National Physical Laboratory NPL (Teddington, UK). After the calibration, the radiance accuracy of different channels was between ±4 % when evaluated with independent test data. Sensors response to radiance proved to be highly linear and was on average 0.9994 for all channels. The spectral response calibration showed side peaks on several channels that were due to the multiple orders of interference of the FPI and highlighted the importance of accurate calibration. The drone-based direct reflectance measurement system showed promising results with imagery collected over Jokioinen agricultural grass test site, Finland. AIRS-based image- and band wise image adjustment provided homogenous and seamless image mosaics even under varying illumination conditions and under clouds.

Multi- and hyperspectral cameras on drones can be valuable tools in environmental monitoring. A significant shortcoming complicating their usage in quantitative remote sensing applications is insufficient robust radiometric calibration methods. In a direct reflectance transformation method, the drone is equipped with a camera and an irradiance sensor, allowing transformation of image pixel values to reflectance factors without ground reference data. This method requires the sensors to be calibrated with higher accuracy than what is usually required by the empirical line method (ELM), but consequently it offers benefits in robustness, ease of operation, and ability to be used on Beyond-Visual Line of Sight flights. The objective of this study was to develop and assess a drone-based workflow for direct reflectance transformation and implement it on our hyperspectral remote sensing system. A novel atmospheric correction method is also introduced, using two reference panels, but, unlike in the ELM, the correction is not directly affected by changes in the illumination. The sensor system consists of a hyperspectral camera (Rikola HSI, by Senop) and an onboard irradiance spectrometer (FGI AIRS), which were both given thorough radiometric calibrations. In laboratory tests and in a flight experiment, the FGI AIRS tilt-corrected irradiances had accuracy better than 1.9% at solar zenith angles up to 70°. The system's low-altitude reflectance factor accuracy was assessed in a flight experiment using reflectance reference panels, where the normalized root mean square errors (NRMSE) were less than ±2% for the light panels (25% and 50%) and less than ±4% for the dark panels (5% and 10%). In the high-altitude images, taken at 100–150 m altitude, the NRMSEs without atmospheric correction were within 1.4%–8.7% for VIS bands and 2.0%–18.5% for NIR bands. Significant atmospheric effects appeared already at 50 m flight altitude. The proposed atmospheric correction was found to be practical and it decreased the high-altitude NRMSEs to 1.3%–2.6% for VIS bands and to 2.3%–5.3% for NIR bands. Overall, the workflow was found to be efficient and to provide similar accuracies as the ELM, but providing operational advantages in such challenging scenarios as in forest monitoring, large-scale autonomous mapping tasks, and real-time applications. Tests in varying illumination conditions showed that the reflectance factors of the gravel and vegetation targets varied up to 8% between sunny and cloudy conditions due to reflectance anisotropy effects, while the direct reflectance workflow had better accuracy. This suggests that the varying illumination conditions have to be further accounted for in drone-based in quantitative remote sensing applications.

Made available in DSpace on 2018-12-11T17:15:51Z (GMT). No. of bitstreams: 0 Previous issue date: 2017-12-01 Tekes Academy of Finland A recent revolution in miniaturised sensor technology has provided markets with novel hyperspectral imagers operating in the frame format principle. In the case of unmanned aerial vehicle (UAV) based remote sensing, the frame format technology is highly attractive in comparison to the commonly utilised pushbroom scanning technology, because it offers better stability and the possibility to capture stereoscopic data sets, bringing an opportunity for 3D hyperspectral object reconstruction. Tuneable filters are one of the approaches for capturing multi- or hyperspectral frame images. The individual bands are not aligned when operating a sensor based on tuneable filters from a mobile platform, such as UAV, because the full spectrum recording is carried out in the time-sequential principle. The objective of this investigation was to study the aspects of band registration of an imager based on tuneable filters and to develop a rigorous and efficient approach for band registration in complex 3D scenes, such as forests. The method first determines the orientations of selected reference bands and reconstructs the 3D scene using structure-from-motion and dense image matching technologies. The bands, without orientation, are then matched to the oriented bands accounting the 3D scene to provide exterior orientations, and afterwards, hyperspectral orthomosaics, or hyperspectral point clouds, are calculated. The uncertainty aspects of the novel approach were studied. An empirical assessment was carried out in a forested environment using hyperspectral images captured with a hyperspectral 2D frame format camera, based on a tuneable Fabry-Pérot interferometer (FPI) on board a multicopter and supported by a high spatial resolution consumer colour camera. A theoretical assessment showed that the method was capable of providing band registration accuracy better than 0.5-pixel size. The empirical assessment proved the performance and showed that, with the novel method, most parts of the band misalignments were less than the pixel size. Furthermore, it was shown that the performance of the band alignment was dependent on the spatial distance from the reference band. Department of Remote Sensing and Photogrammetry Finnish Geospatial Research Institute FGI, Geodeetinrinne 2, FI-02430 Masala Department of Cartography São Paulo State University UNESP, Rua Roberto Simonsen 305, 19060-900 Presidente Prudente Department of Cartography São Paulo State University UNESP, Rua Roberto Simonsen 305, 19060-900 Presidente Prudente Tekes: 2208/31/2013 Academy of Finland: 273806

Multispectral and hyperspectral imaging is usually acquired by satellite and aircraft platforms. Recently, miniaturized hyperspectral 2D frame cameras have showed great potential to precise agriculture estimations and they are feasible to combine with lightweight platforms, such as drones. Drone platform is a flexible tool for remote sensing applications with environment and agriculture. The assessment and comparison of different platforms such as satellite, aircraft and drones with different sensors, such as hyperspectral and RGB cameras is an important task in order to understand the potential of the data provided by these equipment and to select the most appropriate according to the user applications and requirements. In this context, open and permanent test fields are very significant and helpful experimental environment, since they provide a comparative data for different platforms, sensors and users, allowing multi-temporal analyses as well. Objective of this work was to investigate the feasibility of an open permanent test field in context of precision agriculture. Satellite (Sentinel-2), aircraft and drones with hyperspectral and RGB cameras were assessed in this study to estimate biomass, using linear regression models and in-situ samples. Spectral data and 3D information were used and compared in different combinations to investigate the quality of the models. The biomass estimation accuracies using linear regression models were better than 90 % for the drone based datasets. The results showed that the use of spectral and 3D features together improved the estimation model. However, estimation of nitrogen content was less accurate with the evaluated remote sensing sensors. The open and permanent test field showed to be suitable to provide an accurate and reliable reference data for the commercial users and farmers.

Low cost imaging and positioning sensors are opening new frontiers for applications in near real-time Photogrammetry. Omnidirectional cameras acquiring images with 360° coverage, when combined with information coming from GNSS (Global Navigation Satellite Systems) and IMU (Inertial Measurement Unit), can efficiently estimate orientation and object space structure. However, several challenges remain in the use of low-cost sensors and image observations acquired by sensors with non-perspective inner geometry. The accuracy of the measurement using low-cost sensors is affected by different sources of errors and sensor stability. Microelectromechanical systems (MEMS) present a large gap between predicted and actual accuracy. This work presents a study on the performance of an integrated sensor orientation approach to estimate sensor orientation and 3D sparse point cloud, using an incremental bundle adjustment strategy and data coming from a low-cost portable mobile terrestrial system composed by off-theshelf navigation systems and a poly-dioptric system (Ricoh Theta S). Experiments were performed in an outdoor area (sidewalk), achieving a trajectory positional accuracy of 0.33 m and a meter level 3D reconstruction.

Made available in DSpace on 2020-12-12T01:50:04Z (GMT). No. of bitstreams: 0 Previous issue date: 2019-12-01 Mobile mapping systems (MMS) are increasingly used for many photogrammetric and computer vision applications, especially encouraged by the fast and accurate geospatial data generation. The accuracy of point position in an MMS is mainly dependent on the quality of calibration, accuracy of sensor synchronization, accuracy of georeferencing and stability of geometric configuration of space intersections. In this study, we focus on multi-camera calibration (interior and relative orientation parameter estimation) and MMS calibration (mounting parameter estimation). The objective of this study was to develop a practical scheme for rigorous and accurate system calibration of a photogrammetric mapping station equipped with a multi-projective camera (MPC) and a global navigation satellite system (GNSS) and inertial measurement unit (IMU) for direct georeferencing. The proposed technique is comprised of two steps. Firstly, interior orientation parameters of each individual camera in an MPC and the relative orientation parameters of each cameras of the MPC with respect to the first camera are estimated. In the second step the offset and misalignment between MPC and GNSS/IMU are estimated. The global accuracy of the proposed method was assessed using independent check points. A correspondence map for a panorama is introduced that provides metric information. Our results highlight that the proposed calibration scheme reaches centimeter-level global accuracy for 3D point positioning. This level of global accuracy demonstrates the feasibility of the proposed technique and has the potential to fit accurate mapping purposes. Department of Remote Sensing and Photogrammetry of the Finnish Geospatial Research Institute FGI, Geodeetinrinne 2 Department of Computer Science University of Helsinki Cartographic Department School of Technology and Sciences São Paulo State University (UNESP) Department of Built Environment School of Engineering Aalto University National Land Survey of Finland, Opastinsilta 12C Department of Geography and Geology University of Turku Cartographic Department School of Technology and Sciences São Paulo State University (UNESP)

Made available in DSpace on 2020-12-12T01:44:19Z (GMT). No. of bitstreams: 0 Previous issue date: 2019-11-01 Lightweight hyperspectral cameras based on frame geometry have been used for several applications in unmanned aerial vehicles (UAVs). The camera used in this investigation is based on a tunable Fabry-Pérot interferometer (FPI) and works on the time-sequential principle for band acquisition. Due to this feature, when collecting images in movement, hypercubes are generated with unregistered bands, and consequently, the individual bands in each hypercube have different exterior orientation parameters (EOPs), which must be estimated by an image orientation procedure. The objective of this paper was to develop an approach for bundle block adjustment (BBA) using time-dependent polynomial models for simultaneous image orientation of all bands. The procedure consists of using a minimum number of bands to estimate the polynomial parameters. From the estimated polynomial parameters, the EOPs (position and attitude) of all bands can be determined. In tests with backprojecting ground points to interpolated bands, the average error was smaller than 1 pixel, which indicates excellent potential for orthomosaic generation. The polynomial technique was also compared to the conventional BBA. The discrepancies assessed at checkpoints indicated a similar error for both techniques, which were approximately less than the pixel size in planimetry and less than 2.8 times the pixel size in height. Therefore, the results show that the spectral band orientation can be performed with the proposed technique, assuming that the trajectory during the cube can be modeled with the polynomial model, which reduces the workload while achieving the same accuracy as conventional BBA for all bands. Department of Statistics São Paulo State University (UNESP) Department of Cartography São Paulo State University (UNESP) Graduation Program in Cartographic Sciences São Paulo State University (UNESP) Finnish Geospatial Research Institute (FGI) Department of Statistics São Paulo State University (UNESP) Department of Cartography São Paulo State University (UNESP) Graduation Program in Cartographic Sciences São Paulo State University (UNESP)

This Special Issue hosts papers on the integrated use of spectral imaging and 3D technologies in remote sensing, including novel sensors, evolving machine learning technologies for data analysis, and the utilization of these technologies in a variety of geospatial applications. The presented results showed improved results when multimodal data was used in object analysis., Remote Sensing, 11 (14), ISSN:2072-4292