Calibration of laser scanning intensity data using brightness targets

The paper examines a radiometric calibration method used at the Finnish Geodetic Institute (FGI). The brightness calibration targets and calibration scheme of airborne laser scanner intensity data is observed. For calibrating laser scanner intensity data, FGI has developed a system that contains portable brightness targets (tarps) with nominal reflectance from 5% to 70%. Also commercially available gravels and sands were tested for the use of calibration. A laboratory system was set up to measure intensity values under controlled conditions. The paper introduces a concept of calibrating ALS intensity data developed at FGI. Article in English Lazerinio skenavimo intensyvumo duomenų kalibravimas taikant šviesos taikinius Santrauka. Aprašomas Suomijos geodezijos institute taikomas radijometrinis kalibravimo metodas. Analizuojama stebėtų kalibravimo taikinių šviesumas ir lazerinio skenavimo intensyvumo duomenys. Lazerinio skenavimo intensyvumui kalibruoti institute sukurta sistema, kurią sudaro kilnojami šviesos taikiniai su nominaliniu atspindžiu nuo 5 % iki 70 %. Kalibravimo tikslams taip pat išbandyti taikiniai iš įprastinių žvyrų ir smėlių. Intensyvumo reikšmėms išmatuoti kontroliuojamose sąlygose buvo įkurta speciali laboratorija.

Use of Naturally Available Reference Targets to Calibrate Airborne Laser Scanning Intensity Data

We have studied the possibility of calibrating airborne laser scanning (ALS) intensity data, using land targets typically available in urban areas. For this purpose, a test area around Espoonlahti Harbor, Espoo, Finland, for which a long time series of ALS campaigns is available, was selected. Different target samples (beach sand, concrete, asphalt, different types of gravel) were collected and measured in the laboratory. Using tarps, which have certain backscattering properties, the natural samples were calibrated and studied, taking into account the atmospheric effect, incidence angle and flying height. Using data from different flights and altitudes, a time series for the natural samples was generated. Studying the stability of the samples, we could obtain information on the most ideal types of natural targets for ALS radiometric calibration. Using the selected natural samples as reference, the ALS points of typical land targets were calibrated again and examined. Results showed the need for more accurate ground reference data, before using natural samples in ALS intensity data calibration. Also, the NIR camera-based field system was used for collecting ground reference data. This system proved to be a good means for collecting in situ reference data, especially for targets with inhomogeneous surface reflection properties

Absolute Radiometric Calibration of ALS Intensity Data: Effects on Accuracy and Target Classification

Radiometric calibration of airborne laser scanning (ALS) intensity data aims at retrieving a value related to the target scattering properties, which is independent on the instrument or flight parameters. The aim of a calibration procedure is also to be able to compare results from different flights and instruments, but practical applications are sparsely available, and the performance of calibration methods for this purpose needs to be further assessed. We have studied the radiometric calibration with data from three separate flights and two different instruments using external calibration targets. We find that the intensity data from different flights and instruments can be compared to each other only after a radiometric calibration process using separate calibration targets carefully selected for each flight. The calibration is also necessary for target classification purposes, such as separating vegetation from sand using intensity data from different flights. The classification results are meaningful only for calibrated intensity data

The Application of an Unmanned Aerial System and Machine Learning Techniques for Red Clover-Grass Mixture Yield Estimation under Variety Performance Trials

A significant trend has developed with the recent growing interest in the estimation of aboveground biomass of vegetation in legume-supported systems in perennial or semi-natural grasslands to meet the demands of sustainable and precise agriculture. Unmanned aerial systems (UAS) are a powerful tool when it comes to supporting farm-scale phenotyping trials. In this study, we explored the variation of the red clover-grass mixture dry matter (DM) yields between temporal periods (one- and two-year cultivated), farming operations [soil tillage methods (STM), cultivation methods (CM), manure application (MA)] using three machine learning (ML) techniques [random forest regression (RFR), support vector regression (SVR), and artificial neural network (ANN)] and six multispectral vegetation indices (VIs) to predict DM yields. The ML evaluation results showed the best performance for ANN in the 11-day before harvest category (R2 = 0.90, NRMSE = 0.12), followed by RFR (R2 = 0.90 NRMSE = 0.15), and SVR (R2 = 0.86, NRMSE = 0.16), which was furthermore supported by the leave-one-out cross-validation pre-analysis. In terms of VI performance, green normalized difference vegetation index (GNDVI), green difference vegetation index (GDVI), as well as modified simple ratio (MSR) performed better as predictors in ANN and RFR. However, the prediction ability of models was being influenced by farming operations. The stratified sampling, based on STM, had a better model performance than CM and MA. It is proposed that drone data collection was suggested to be optimum in this study, closer to the harvest date, but not later than the ageing stage

An Automated Machine Learning Framework in Unmanned Aircraft Systems:New Insights into Agricultural Management Practices Recognition Approaches

The recent trend of automated machine learning (AutoML) has been driving further significant technological innovation in the application of artificial intelligence from its automated algorithm selection and hyperparameter optimization of the deployable pipeline model for unraveling substance problems. However, a current knowledge gap lies in the integration of AutoML technology and unmanned aircraft systems (UAS) within image-based data classification tasks. Therefore, we employed a state-of-the-art (SOTA) and completely open-source AutoML framework, Auto-sklearn, which was constructed based on one of the most widely used ML systems: Scikit-learn. It was combined with two novel AutoML visualization tools to focus particularly on the recognition and adoption of UAS-derived multispectral vegetation indices (VI) data across a diverse range of agricultural management practices (AMP). These include soil tillage methods (STM), cultivation methods (CM), and manure application (MA), and are under the four-crop combination fields (i.e., red clover-grass mixture, spring wheat, pea-oat mixture, and spring barley). Furthermore, they have currently not been efficiently examined and accessible parameters in UAS applications are absent for them. We conducted the comparison of AutoML performance using three other common machine learning classifiers, namely Random Forest (RF), support vector machine (SVM), and artificial neural network (ANN). The results showed AutoML achieved the highest overall classification accuracy numbers after 1200 s of calculation. RF yielded the second-best classification accuracy, and SVM and ANN were revealed to be less capable among some of the given datasets. Regarding the classification of AMPs, the best recognized period for data capture occurred in the crop vegetative growth stage (in May). The results demonstrated that CM yielded the best performance in terms of classification, followed by MA and STM. Our framework presents new insights into plant–environment interactions with capable classification capabilities. It further illustrated the automatic system would become an important tool in furthering the understanding for future sustainable smart farming and field-based crop phenotyping research across a diverse range of agricultural environmental assessment and management applications

Toward Automated Machine Learning-Based Hyperspectral Image Analysis in Crop Yield and Biomass Estimation

The incorporation of autonomous computation and artificial intelligence (AI) technologies into smart agriculture concepts is becoming an expected scientific procedure. The airborne hyperspectral system with its vast area coverage, high spectral resolution, and varied narrow-band selection is an excellent tool for crop physiological characteristics and yield prediction. However, the extensive and redundant three-dimensional (3D) cube data processing and computation have made the popularization of this tool a challenging task. This research integrated two important open-sourced systems (R and Python) combined with automated hyperspectral narrowband vegetation index calculation and the state-of-the-art AI-based automated machine learning (AutoML) technology to estimate yield and biomass, based on three crop categories (spring wheat, pea and oat mixture, and spring barley with red clover) with multifunctional cultivation practices in northern Europe and Estonia. Our study showed the estimated capacity of the empirical AutoML regression model was significant. The best coefficient of determination (R2) and normalized root mean square error (NRMSE) for single variety planting wheat were 0.96 and 0.12 respectively; for mixed peas and oats, they were 0.76 and 0.18 in the booting to heading stage, while for mixed legumes and spring barley, they were 0.88 and 0.16 in the reproductive growth stages. In terms of straw mass estimation, R2 was 0.96, 0.83, and 0.86, and NRMSE was 0.12, 0.24, and 0.33 respectively. This research contributes to, and confirms, the use of the AutoML framework in hyperspectral image analysis to increase implementation flexibility and reduce learning costs under a variety of agricultural resource conditions. It delivers expert yield and straw mass valuation two months in advance before harvest time for decision-makers. This study also highlights that the hyperspectral system provides economic and environmental benefits and will play a critical role in the construction of sustainable and intelligent agriculture techniques in the upcoming years

Aerolaserskannerimise intensiivsuse parandamine ja kalibreerimine looduslikke pindasid kasutades

Airborne laser scanning (ALS) has been used widely due to its three dimensional information. Additionally to coordinates, ALS systems record also intensity values that describe the objects backscattering properties. The usage of ALS intensity data has been problematic due to the lack of information for correcting itnensity values for different affects, and also how to calibrate intensity data. Doctoral thesis of Ants Vain focuses on the usage of naturally availabel targets (e.g. asphalt, concrete etc.) of the measured area to use them as calibration targets. The tests carried out for this Thesis showed that the asphalt proved to be the most stable target and produced quite good results when used as calibration target. A near infrared camera was also tested to obtain objects backscattering properties. The camera solution proved to be an useful tool since it can be used simultaneously when the flights take place, therefore it provides results that are closer to actual ones than those measured in the laboratory. The samples will dry in the laboratory, therefore their backscattering properties will change also. Previous tests showed that naturally available targets can be used as calibrators. Since the ALS measurements are one of the remote sensing techiques, the intensity data has to be corrected for several factors before the calibration procedure. This Thesis also studied the range and topography influence on the ALS intensity data. Since the targets are assumed to have a Lambertian backscattering properties, the backscattered energy will reduce with the growth of incidence angel (the angle between incoming laser bean and surface normal). The tests showed the the 20 degree incidence angle will reduce the intnesity values up to 8%. The moisture level in the surface will reduce the intensity values. The study conducted within this Thesis showed that the moisture can reduce the intensity up to 30%. To obtain the moisture level in the surface the near infrared camera was used. The results were similar as in the test that was carried out in the laboratory. The Leica ALS50-II uses Automatic Gain Control (AGC), that is used to ramp up the weak returns or ramp the too strong signals down. In other words, the AGC will keep the received signal in the sensitive area of the receiver. The study conducted within this Thesis showed that the ALS intensity follows the AGC trend. The result of this study was a linear correction model that minimizes the effect of AGC on the intensity values. The doctoral thesis of Ants Vain is one of the few in the world and the first in Estonia that studies the correction and calibration of ALS intensity data. The knowledge that was collected thorughout the studies that were carried out in this Thesis can be used in further studies were ALS intensity data plays a relevant part.Aerolaserskaneerimine (ALS) on leidnud laialdast kasutust just oma kolmemõõtmelise informatsiooni tõttu. Lisaks kolmele koordinaadile salvestab ALS süsteem ka intensiivsusväärtuseid, mis iseloomustavad uuritava objekti radiomeetrilisi omadusi. Seniajani on ALS intensiivsuse kasutamine rakendusuuringutes olnud raskendatud, kuna puudub selge kontseptsioon intensiivsuse korrigeerimiseks erinevate tegurite mõju vastu ning kuidas intensiivsusväärtuseid kalibreerida Ants Vain’u doktoritöö keskendub sellele, kuidas on võimalik mõõdistusalal ära kasutada olemasolevaid pindasid (nt asfalt, betton jne), et neid kasutada intensiivsuse kalibreerimiseks. Doktoritöös läbiviidud katsed näitasid, et asfalt osutus kõige stabiilsemaks pinnaseks ning selle kasutamine kalibratsioonis andis häid tulemusi. Samuti testiti lähiinfrapunases spektripiirkonnas töötavat käsifotokaamerat, mille abil määrati pindade tagasihajumise omadusi. Kaamera osutus kasulikuks just seetõttu, et mõõdistusi saab teha samaaeglselt ülelennuga, mis tähendab, et ilmastikutingimused on lähedasemad tegelikkusele, kui seda oleks laboris teostatava mõõtmiste puhul. Laboris olev proovitükk paratamatult muudab oma tagasihajumise omadusi just niiskuskaotuse tõttu, mida kaamera puhul pole karta. Eelnevalt läbiviidud katsed näitasid, et looduslikult kättesaadavaid pindasid on võimalik kasutada kalibreerimises. Enne kalibreerimist tuleks intensiivsust korrigeerida erinevate tegurite vastu, kuna tegemist on ikkagi kaugseire meetodiga. Antud doktoritöö uuris ka topograafia ja kauguse mõju intensiivsusväärtustele. Kuna ALS puhul eeldatakse, et pindadel on Lamberti tagasihajumise omadused, tähendab see seda, et mida suurem on laserkiire kokkupuutenurk pinnaga, seda vähem energiat tagasi hajub. Läbi viidud katse näitas, et kokkupuutenurga puhul kuni 20 kraadi on selle mõju intensiivsusele kuni 8%. Niiskuse tase pinnases alandab samuti intensiivsusväärtuseid. Doktoritöö raames läbi viidud katse näitas, et erinevus võib ulatuda isegi kuni 30%-ni. Ka niiskuse taseme määramiseks pinnases testiti lähiinfrapunases spektripiirkonnas töötavat käsifotokaamerat, mille tulemused langesid kokku laboris saadud katse tulemsutega. Leica ALS50-II juures kasutatav seade, AGC (Automatic Gain Control), on mõeldud selleks, et võimendada liiga nõrka tagasihajunud signaali või siis vähendada liiga tugevat signaal. Teisisõnu, hoiab ta vastuvõetud signaali vastuvõtja tundlikus piirkonnas. Doktoritöö raames läbi viidud uurimus näitas, et intensiivsus järgib AGC trendi. Uurimustöö tulemusena pakuti välja lineaarne parandusmudel AGC mõju vähendamiseks intensiivsusväärtustele. Ants Vain’u doktoritöö on üks väheseid maailmas ja esimesi Eestis, mis tegeleb ALS intensiivsuse parandamisega ja kalibreerimisega. Doktoritöö raames teostatud uurimustest saadud teadmisi on võimalik kasutata intensiivsusväärtuste parandamiseks, et neid edaspidistes rakendusuuringustes kasutada.Estonian University of Life Sciences, Doctoral School in the Field of Building and Environmental Engineerin

The Potential of Optical UAS Data for Predicting Surface Soil Moisture in a Peatland across Time and Sites

Advances in unmanned aerial systems (UASs) have increased the potential of remote sensing to overcome scale issues for soil moisture (SM) quantification. Regardless, optical imagery is acquired using various sensors and platforms, resulting in simpler operations for management purposes. In this respect, we predicted SM at 10 cm depth using partial least squares regression (PLSR) models based on optical UAS data and assessed the potential of this framework to provide accurate predictions across dates and sites. For this, we evaluated models’ performance using several datasets and the contribution of spectral and photogrammetric predictors on the explanation of SM. The results indicated that our models predicted SM at comparable accuracies as other methods relying on more expensive and complex sensors; the best R2 was 0.73, and the root-mean-squared error (RMSE) was 13.1%. Environmental conditions affected the predictive importance of different metrics; photogrammetric-based metrics were relevant over exposed surfaces, while spectral predictors were proxies of water stress status over homogeneous vegetation. However, the models demonstrated limited applicability across times and locations, particularly in highly heterogeneous conditions. Overall, our findings indicated that integrating UAS imagery and PLSR modelling is suitable for retrieving SM measures, offering an improved method for short-term monitoring tasks