Drone-based spectral and 3D remote sensing applications for forestry and agriculture

Practising sustainable agriculture and forestry requires information on the state of forests and crops to support management. In precision agriculture, crops are observed in order to treat them precisely in the right place and at the right time, saving both production costs and the environment. Similarly, in forests, information on the composition and state of forest health are crucial to enable their sustainable management. In particular, climate-change-driven insect pests have increased, but economic and ecological losses can be reduced by the right actions if up-to-date and precise information on the health of forests is available. In recent years, drones with cameras have evolved into a flexible way to collect remote sensing data locally. Spectral cameras provide accurate information about the reflection properties of objects, and photogrammetric methods also provide a cost-effective way to collect three-dimensional (3D) data from an object. The objective of this work was to develop and assess drone-based 3D and spectral remote sensing techniques to classify the health status of individual trees and to estimate crop biomass, various biochemical parameters such as nitrogen content, and grass-feeding quality. The work developed a processing chain in which spectral and 3D features were extracted from remote sensing data. Then, combining the features with observations and reference measurements collected from plants, machine learning models were developed for tree health classification and estimation of crop-related parameters. The effects of different factors related to data collection and processing on classification and estimation accuracies were studied in order to generate knowledge on optimal sensors and methods. In general, radiometric corrections, spectral resolution, and the combined use of spectral and 3D features improved classification and estimation accuracies. However, the optimal sensors as well as the data collection and processing methods depend on the different applications and their accuracy requirements. This work was the first to demonstrate the ability of drone hyperspectral data to map the health status of a forest by classifying individual trees infested by bark beetles. The results of the work also showed that drone-based mapping offers a great tool to estimate agricultural crop parameters which can be applied to the optimization of various precision agriculture tasks.Kestävän maa- ja metsätalouden harjoittaminen vaatii tietoa metsien ja viljelykasvien tilasta päätöksenteon tueksi. Täsmämaataloudessa viljelykasveja havainnoidaan, jotta viljelytoimenpiteet voidaan kohdistaa oikeaan paikkaan ja oikea-aikaisesti säästäen sekä tuotantokustannuksia että ympäristöä. Metsissä tieto metsien terveydentilasta on tärkeää, jotta voidaan hillitä metsätuhojen leviämistä. Erityisesti hyönteistuhot ovat lisääntyneet voimakkaasti ilmastonmuutoksen vauhdittamana, mutta taloudellisia ja ekologisia tappiota voidaan vähentää oikeilla toimenpiteillä, jos on olemassa ajantasaisesta tietoa metsien terveydentilasta. Dronet ja niihin asennettavat kamerat ovat kehittyneet viime vuosina joustavaksi tavaksi kerätä kaukokartoitusaineistoa paikallisesti. Spektrikameroilla saadaan tarkkaa tietoa kohteen heijastusominaisuuksista, ja fotogrammetriset menetelmät mahdollistavat myös kustannustehokkaan tavan kerätä kohteesta kolmiulotteista (3D) tietoa. Tämän työn tavoitteena oli kehittää näihin aineistoihin nojautuen kaukokartoitusmenetelmiä yksittäisten puiden terveydentilan luokitteluun sekä viljelykasvien biomassan, erilaisten biokemiallisten parametrien, kuten typpipitoisuuden sekä nurmen ruokintalaadun, kuten D-arvon estimointiin. Työssä kehitettiin prosessointiketju, jossa kaukokartoitusaineistoista irrotettiin spektri- ja 3D-piirteitä, yhdistettiin ne kasveista kerättyihin havaintoihin ja mittauksiin sekä muodostettiin koneoppimismalleja puiden luokittelua ja viljelykasveihin liittyvien parametrien estimointia varten. Työssä verrattiin useiden aineistonkeräykseen ja -prosessointiin liittyvien tekijöiden vaikutuksia luokittelu- ja estimointitulosten tarkkuuteen optimaalisten menetelmien löytämiseksi. Esimerkiksi spektri- ja 3D-piirteiden hyödyntäminen yhdessä sekä radiometriset korjaukset paransivat yleisesti luokittelu- ja estimointitarkkuuksia. Optimaaliset sensorit sekä aineistonkeräys- ja käsittelytavat riippuvat kuitenkin eri sovelluksista ja niiden tarkkuusvaatimuksista. Työssä osoitettiin ensimmäistä kertaa dronesta kerätyn hyperspektrisen aineiston kyvykkyys metsän terveydentilan havainnoinnissa luokittelemalla kuuset kolmeen luokkaan kirjanpainajan aiheuttaman tuhon perusteella. Työn tulokset myös osoittivat drone-pohjaisen kartoituksen kyvyn estimoida erilaisia viljelykasvien parametreja, joita voidaan edelleen soveltaa suunniteltaessa esimerkiksi lisälannoitusta tai säilörehun optimaalista korjuuaikaa

A Novel Machine Learning Method for Estimating Bio-mass of Grass Swards Using a Photogrammetric Canopy Height Model, Images and Vegetation Indices Captured by a Drone

-Agriculture, vol. 8, issue 5, 2018. - This article belongs to the Special Issue Remote Sensing in Agricultural System

Geometric and Reflectance Signature Characterization of Complex Canopies Using Hyperspectral Stereoscopic Images from Uav and Terrestrial Platforms

Peer reviewe

Estimating Biomass and Nitrogen Amount of Barley and Grass Using UAV and Aircraft Based Spectral and Photogrammetric 3D Features

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Remote sensing of bark beetle damage in urban forests at individual tree level using a novel hyperspectral camera from UAV and aircraft

Climate-related extended outbreaks and range shifts of destructive bark beetle species pose a serious threat to urban boreal forests in North America and Fennoscandia. Recent developments in low-cost remote sensing technologies offer an attractive means for early detection and management of environmental change. They are of great interest to the actors responsible for monitoring and managing forest health. The objective of this investigation was to develop, assess, and compare automated remote sensing procedures based on novel, low-cost hyperspectral imaging technology for the identification of bark beetle infestations at the individual tree level in urban forests. A hyperspectral camera based on a tunable Fabry-Perot interferometer was operated from a small, unmanned airborne vehicle (UAV) platform and a small Cessna-type aircraft platform. This study compared aspects of using UAV datasets with a spatial extent of a few hectares (ha) and a ground sample distance (GSD) of 10-12 cm to the aircraft data covering areas of several km(2) and having a GSD of 50 cm. An empirical assessment of the automated identification of mature Norway spruce (Picea abies L. Karst.) trees suffering from infestation (representing different colonization phases) by the European spruce bark beetle (Ips typographus L.) was carried out in the urban forests of Lahti, a city in southern Finland. Individual spruces were classified as healthy, infested, or dead. For the entire test area, the best aircraft data results for overall accuracy were 79% (Cohen's kappa: 0.54) when using three crown color classes (green as healthy, yellow as infested, and gray as dead). For two color classes (healthy, dead) in the same area, the best overall accuracy was 93% (kappa: 0.77). The finer resolution UAV dataset provided better results, with an overall accuracy of 81% (kappa: 0.70), compared to the aircraft results of 73% (kappa: 0.56) in a smaller sub-area. The results showed that novel, low-cost remote sensing technologies based on individual tree analysis and calibrated remote sensing imagery offer great potential for affordable and timely assessments of the health condition of vulnerable urban forests.Peer reviewe

Direct reflectance transformation methodology for drone-based hyperspectral imaging

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 lowaltitude 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

Multispectral Imagery Provides Benefits for Mapping Spruce Tree Decline Due to Bark Beetle Infestation When Acquired Late in the Season

Climate change is increasing pest insects’ ability to reproduce as temperatures rise, resulting in vast tree mortality globally. Early information on pest infestation is urgently needed for timely decisions to mitigate the damage. We investigated the mapping of trees that were in decline due to European spruce bark beetle infestation using multispectral unmanned aerial vehicles (UAV)-based imagery collected in spring and fall in four study areas in Helsinki, Finland. We used the Random Forest machine learning to classify trees based on their symptoms during both occasions. Our approach achieved an overall classification accuracy of 78.2% and 84.5% for healthy, declined and dead trees for spring and fall datasets, respectively. The results suggest that fall or the end of summer provides the most accurate tree vitality classification results. We also investigated the transferability of Random Forest classifiers between different areas, resulting in overall classification accuracies ranging from 59.3% to 84.7%. The findings of this study indicate that multispectral UAV-based imagery is capable of classifying tree decline in Norway spruce trees during a bark beetle infestation

UAV-based photogrammetric point clouds and hyperspectral imaging for mapping biodiversity indicators in boreal forests

Peer reviewe

Direct reflectance measurements from drones: sensor absolute radiometric calibration and system tests for forest reflectance characterization

Drone-based remote sensing has evolved rapidly in recent years. Miniaturized hyperspectral imaging sensors are becoming more common as they provide more abundant information of the object compared to traditional cameras. Reflectance is a physically defined object property and therefore often preferred output of the remote sensing data capture to be used in the further processes. Absolute calibration of the sensor provides a possibility for physical modelling of the imaging process and enables efficient procedures for reflectance correction. Our objective is to develop a method for direct reflectance measurements for drone-based remote sensing. It is based on an imaging spectrometer and irradiance spectrometer. This approach is highly attractive for many practical applications as it does not require in situ reflectance panels for converting the sensor radiance to ground reflectance factors. We performed SI-traceable spectral and radiance calibration of a tuneable Fabry-Pérot Interferometer -based (FPI) hyperspectral camera at the National Physical Laboratory NPL (Teddington, UK). The camera represents novel technology by collecting 2D format hyperspectral image cubes using time sequential spectral scanning principle. The radiance accuracy of different channels varied between ±4% when evaluated using independent test data, and linearity of the camera response was on average 0.9994. The spectral response calibration showed side peaks on several channels that were due to the multiple orders of interference of the FPI. The drone-based direct reflectance measurement system showed promising results with imagery collected over Wytham Forest (Oxford, UK)