An operational radiometric correction technique for shadow reduction in multispectral uav imagery

This study focuses on the recovery of information from shadowed pixels in RGB or multispectral imagery sensed from unmanned aerial vehicles (UAVs). The proposed technique is based on the concept that a property characterizing a given surface is its spectral reflectance, i.e., the ratio between the flux reflected by the surface and the radiant flux received by the surface, and this ratio is usually similar under direct-plus-diffuse irradiance and under diffuse irradiance when a Lambertian behavior can be assumed. Scene-dependent elements, such as trees, shrubs, man-made constructions, or terrain relief, can block part of the direct irradiance (usually sunbeams), in which part of the surface only receives diffuse irradiance. As a consequence, shadowed surfaces comprising pixels of the image created by the UAV remote sensor appear. Regardless of whether the imagery is analyzed by means of photointerpretation or digital classification methods, when the objective is to create land cover maps, it is hard to treat these areas in a coherent way in terms of the areas receiving direct and diffuse irradiance. The hypothesis of the present work is that the relationship between irradiance conditions in shadowed areas and non-shadowed areas can be determined by following classical empirical line techniques for fulfilling the objective of a coherent treatment in both kinds of areas. The novelty of the presented method relies on the simultaneous recovery of information in non-shadowed and shadowed areas by the in situ spectral reflectance measurements of characterized Lambertian targets followed by smoothing of the penumbra area. Once in the lab, firstly, we accurately detected the shadowed pixels by combining two well-known techniques for the detection of the shadowed areas: (1) using a physical approach based on the sun's position and the digital surface model of the area covered by the imagery; and (2) the image-based approach using the histogram properties of the intensity image. In this paper, we present the benefits of the combined usage of both techniques. Secondly, we applied a fit between non-shadowed and shadowed areas by using a twin set of spectrally characterized target sets. One set was placed under direct and diffuse irradiance (non-shadowed targets), whereas the second set (with the same spectral characteristics) was placed under diffuse irradiance (shadowed targets). Assuming that the reflectance of the homologous targets of each set was the same, we approximated the diffuse incoming irradiance through an empirical line correction. The model was applied to all detected shadowed areas in the whole scene. Finally, a smoothing filter was applied to the penumbra transitions. The presented empirical method allowed the operational and coherent recovery of information from shadowed areas, which is very common in high-resolution UAV imagery

The agricultural impact of the 2015–2016 floods in Ireland as mapped through Sentinel 1 satellite imagery

peer-reviewedIrish Journal of Agricultural and Food Research | Volume 58: Issue 1 The agricultural impact of the 2015–2016 floods in Ireland as mapped through Sentinel 1 satellite imagery R. O’Haraemail , S. Green and T. McCarthy DOI: https://doi.org/10.2478/ijafr-2019-0006 | Published online: 11 Oct 2019 PDF Abstract Article PDF References Recommendations Abstract The capability of Sentinel 1 C-band (5 cm wavelength) synthetic aperture radio detection and ranging (RADAR) (abbreviated as SAR) for flood mapping is demonstrated, and this approach is used to map the extent of the extensive floods that occurred throughout the Republic of Ireland in the winter of 2015–2016. Thirty-three Sentinel 1 images were used to map the area and duration of floods over a 6-mo period from November 2015 to April 2016. Flood maps for 11 separate dates charted the development and persistence of floods nationally. The maximum flood extent during this period was estimated to be ~24,356 ha. The depth of rainfall influenced the magnitude of flood in the preceding 5 d and over more extended periods to a lesser degree. Reduced photosynthetic activity on farms affected by flooding was observed in Landsat 8 vegetation index difference images compared to the previous spring. The accuracy of the flood map was assessed against reports of flooding from affected farms, as well as other satellite-derived maps from Copernicus Emergency Management Service and Sentinel 2. Monte Carlo simulated elevation data (20 m resolution, 2.5 m root mean square error [RMSE]) were used to estimate the flood’s depth and volume. Although the modelled flood height showed a strong correlation with the measured river heights, differences of several metres were observed. Future mapping strategies are discussed, which include high–temporal-resolution soil moisture data, as part of an integrated multisensor approach to flood response over a range of spatial scales

Assessing the impact of illumination on UAV pushbroom hyperspectral imagery collected under various cloud cover conditions

The recent development of small form-factor (<6 kg), full range (400–2500 nm) pushbroom hyperspectral imaging systems (HSI) for unmanned aerial vehicles (UAV) poses a new range of opportunities for passive remote sensing applications. The flexible deployment of these UAV-HSI systems have the potential to expand the data acquisition window to acceptable (though non-ideal) atmospheric conditions. This is an important consideration for time-sensitive applications (e.g. phenology) in areas with persistent cloud cover. Since the majority of UAV studies have focused on applications with ideal illumination conditions (e.g. minimal or non-cloud cover), little is known to what extent UAV-HSI data are affected by changes in illumination conditions due to variable cloud cover. In this study, we acquired UAV pushbroom HSI (400–2500 nm) over three consecutive days with various illumination conditions (i.e. cloud cover), which were complemented with downwelling irradiance data to characterize illumination conditions and in-situ and laboratory reference panel measurements across a range of reflectivity (i.e. 2%, 10%, 18% and 50%) used to evaluate reflectance products. Using these data we address four fundamental aspects for UAV-HSI acquired under various conditions ranging from high (624.6 ± 16.63 W·m2) to low (2.5 ± 0.9 W·m2) direct irradiance: atmospheric compensation, signal-to-noise ratio (SNR), spectral vegetation indices and endmembers extraction. For instance, two atmospheric compensation methods were applied, a radiative transfer model suitable for high direct irradiance, and an Empirical Line Model (ELM) for diffuse irradiance conditions. SNR results for two distinctive vegetation classes (i.e. tree canopy vs herbaceous vegetation) reveal wavelength dependent attenuation by cloud cover, with higher SNR under high direct irradiance for canopy vegetation. Spectral vegetation index (SVIs) results revealed high variability and index dependent effects. For example, NDVI had significant differences (p < 0.05) across illumination conditions, while NDWI appeared insensitive at the canopy level. Finally, often neglected diffuse illumination conditions may be beneficial for revealing spectral features in vegetation that are obscured by the predominantly non-Lambertian reflectance encountered under high direct illumination. To our knowledge, our study is the first to use a full range pushbroom UAV sensor (400–2500 nm) for assessing illumination effects on the aforementioned variables. Our findings pave the way for understanding the advantages and limitations of ultra-high spatial resolution full range high fidelity UAV-HSI for ecological and other applications

Object-based mapping of temperate marine habitats from multi-resolution remote sensing data

PhD ThesisHabitat maps are needed to inform marine spatial planning but current methods of field survey and data interpretation are time-consuming and subjective. Object-based image analysis (OBIA) and remote sensing could deliver objective, cost-effective solutions informed by ecological knowledge. OBIA enables development of automated workflows to segment imagery, creating ecologically meaningful objects which are then classified based on spectral or geometric properties, relationships to other objects and contextual data. Successfully applied to terrestrial and tropical marine habitats for over a decade, turbidity and lack of suitable remotely sensed data had limited OBIA’s use in temperate seas to date. This thesis evaluates the potential of OBIA and remote sensing to inform designation, management and monitoring of temperate Marine Protected Areas (MPAs) through four studies conducted in English North Sea MPAs. An initial study developed OBIA workflows to produce circalittoral habitat maps from acoustic data using sequential threshold-based and nearest neighbour classifications. These methods produced accurate substratum maps over large areas but could not reliably predict distribution of species communities from purely physical data under largely homogeneous environmental conditions. OBIA methods were then tested in an intertidal MPA with fine-scale habitat heterogeneity using high resolution imagery collected by unmanned aerial vehicle. Topographic models were created from the imagery using photogrammetry. Validation of these models through comparison with ground truth measurements showed high vertical accuracy and the ability to detect decimetre-scale features. The topographic and spectral layers were interpreted simultaneously using OBIA, producing habitat maps at two thematic scales. Classifier comparison showed that Random Forests Abstract ii outperformed the nearest neighbour approach, while a knowledge-based rule set produced accurate results but requires further research to improve reproducibility. The final study applied OBIA methods to aerial and LiDAR time-series, demonstrating that despite considerable variability in the data, pre- and post-classification change detection methods had sufficient accuracy to monitor deviation from a background level of natural environmental fluctuation. This thesis demonstrates the potential of OBIA and remote sensing for large-scale rapid assessment, detailed surveillance and change detection, providing insight to inform choice of classifier, sampling protocol and thematic scale which should aid wider adoption of these methods in temperate MPAs.Natural Environment Research Council and Natural Englan

Unmanned Aerial Vehicles for Vegetation Mapping: Opportunities and Challenges

Pflanzen sind eng mit einer Reihe von Ökosystemprozessen und -dienstleistungen wie die Bereitstellung von Lebensmitteln und Trinkwasser, die Klimaregulierung sowie die Bodenbildung und Kohlenstoffspeicherung verbunden. Deshalb können Vegetationseigenschaften wie Artenreichtum, Biodiversität und Pflanzenmerkmale zur Bewertung und Überwachung von Ökosystemprozessen genutzt werden. Die genaue Beobachtung von Vegetationsveränderungen ist daher entscheidend für das Verständnis der aktuellen und zukünftigen Ökosystemdynamik. Fernerkundungsdaten haben hohes Potenzial Vegetationseigenschaften und -prozesse räumlich abzubilden. Die zunehmende Verfügbarkeit von sehr hochauflösenden Fernerkundungsdaten ermöglicht auch die Untersuchung von feinskaligen Prozessen. Die für niedriger aufgelöste Fernerkundungsdaten entwickelten Auswertungsverfahren sind häufig nicht auf sehr hochaufgelöste Daten übertragbar. Daher werden neue Verfahren benötigt, um das volle Potenzial auszuschöpfen. Die Vorteile von sehr hochauflösenden Daten liegen unter anderem in der Erkennung von einzelnen Pflanzen und der besseren räumlichen Feinabstimmung mit Felddaten. Diese Vorteile ermöglichen die genaue Kartierung von Pflanzenarten auf der Ebene einzelner Individuen und Vegetationseigenschaften auf der Ebene von Pflanzengesellschaften, wie die Biodiversität, oberirdische Biomasse oder Artenzusammensetzung. Unbemannte Luftfahrzeuge (UAVs) werden als kostengünstige Plattform zur Gewinnung von Daten mit sehr hoher Auflösung, insbesondere für kleine Gebiete, verwendet. Daher ist ihr Einsatz gut zur Entwicklung neuer Methoden geeignet. Das Ziel dieser Arbeit war die Feststellung von Vorteilen und Limitierungen der Nutzung von UAVs zur Vegetationskartierung. Der Fokus der Arbeit lag auf zwei Hauptthemen, die Kartierung von Pflanzenarten und kleinräumigen Ökosystemprozessen. Eine der Fallstudien zeigte, dass die Verwendung von sehr hochauflösenden Daten zur Klassifizierung von Pflanzenarten durch die Überlappung verschiedener Arten erschwert wird. Daher ist Nutzung solcher Daten zur direkten Kartierung von Grünlandarten nur für Habitate mit geringer Vegetationsbedeckung und einfachen Strukturen, wie beispielsweise Dünenhabitate, vielversprechend. Eine zweite Fallstudie ergab, dass der Schattenwurf von Baumkronen den Erfolg von UAV-basierten Klassifikationen der invasiven Baumarten $\textit{Ulex europaeus}$, $\textit{Acacia dealbata}$ und $\textit{Pinus radiata}$ erheblich beeinflusst. Dabei machte es keinen Unterschied ob optische Daten oder Informationen über die Textur oder Kronenstruktur verwendet wurden. Anhand von Simulationen wurde dargestellt, dass jede Art aufgrund ihrer spezifischen Kronenarchitektur unterschiedliche Schatten erzeugt. Die optimalen Zeitfenster zur Klassifikation im Verlaufe eines Tages unterscheiden sich daher zwischen den einzelnen Arten. In einer dritten Fallstudie wurde gezeigt, dass Merkmale der oberirdischen Vegetation als Proxy genutzt werden können um Kartierungen von unterirdischen Kohlenstoffvorräten in Mooren zu verbessern. Ein empirisches Modell wurde genutzt um unter- und oberirdische Merkmale zu verknüpfen. Dafür wurden kontinuierliche Daten mit Informationen über Höhe, Biomasse, sowie den Artenreichtum und die Artenzusammensetzung der Vegetation verwendet. UAV Daten wurden genutzt um die relevanten oberirdischen Merkmale zu kartieren. Der unterirdische Kohlenstoffvorrat wurde dann durch die Parametrisierung des plotbasierten Modells mit den UAV-Extrapolationen kartiert. Dies deutet darauf hin, dass auch Ökosystemeigenschaften mit geringem direkten Einfluss auf die Reflektanz mit Hilfe von Vegetationsmerkmalen als Proxies kartiert werden können. Da bei Kopplung empirischer Modelle in jedem Modellierungsschritt fehlerbehaftete Voraussagen entstehen können, wird ein solcher Ansatz nur empfohlen, wenn starke empirische Verbindungen zwischen den feldbasierten Variablen vorliegen. Diese Arbeit zeigt, dass mit UAVs erhobene Erdbeobachtungsdaten geeignet sind, um die technischen und umweltbedingten Voraussetungen für eine erfolgreiche Kartierung von Pflanzenarten zu erforschen, um neue Methoden zu entwickeln, welche die Genauigkeit von Klassifikationen aus sehr hochaufgelösten Daten erhöhen und um Vegetationseigenschaften mit unterirdischen Gradienten zu verknüpfen. Die Arbeit enthält außerdem Empfehlungen und Vorschläge für die zukünftige Erforschung von feinskaligen Vegetationsprozessen

Reliably Mapping Low-intensity Forest Disturbance Using Satellite Radar Data

In the last decades tropical forests have experienced increased fragmentation due to a global growing demand for agricultural and forest commodities. Satellite remote sensing offers a valuable tool for monitoring forest loss, thanks to the global coverage and the temporal consistency of the acquisitions. In tropical regions, C-band Synthetic Aperture Radar (SAR) data from the Sentinel-1 mission provides cloud-free and open imagery on a 6- or 12-day repeat cycle, offering the unique opportunity to monitor forest disturbances in a timely and continuous manner. Despite recent advances, mapping subtle forest losses, such as those due to small-scale and irregular selective logging, remains problematic. A Cumulative Sum (CuSum) approach has been recently proposed for forest monitoring applications, with preliminary studies showing promising results. Unfortunately, the lack of accurate in-situ measurements of tropical forest loss has prevented a full validation of this approach, especially in the case of low-intensity logging. In this study, we used high-quality field measurements from the tropical Forest Degradation Experiment (FODEX), combining unoccupied aerial vehicle (UAV) LiDAR, Terrestrial Laser Scanning (TLS), and field-inventoried data of forest structural change collected in two logging concessions in Gabon and Peru. The CuSum algorithm was applied to VV-polarized Sentinel-1 ground range detected (GRD) time series to monitor a range of canopy loss events, from individual tree extraction to forest clear cuts. We developed a single change metric using the maximum of the CuSum distribution, retrieving location, time, and magnitude of the disturbance events. A comparison of the CuSum algorithm with the LiDAR reference map resulted in a 78% success rate for the test site in Gabon and 65% success rate for the test site in Peru, for disturbances as small as 0.01 ha in size and for canopy height losses as fine as 10 m. A correlation between the change metric and above ground biomass (AGB) change was found with R2 = 0.95, and R2 = 0.83 for canopy height loss. From the regression model we directly estimated local AGB loss maps for the year 2020, at 1 ha scale and in percentages of AGB loss. Comparison with the Global Forest Watch (GFW) Tree Cover Loss (TCL) product showed a 61% overlap between the two maps when considering only deforested pixels, with 504 ha of deforestation detected by CuSum vs. 348 ha detected by GFW. Low intensity disturbances captured by the CuSum method were largely undetected by GFW and by the SAR-based Radar for Detecting Deforestation (RADD) Alert System. The results of this study confirm this approach as a simple and reproducible change detection method for monitoring and quantifying fine-scale to high intensity forest disturbances, even in the case of multi-storied and high biomass forests

Derivation of forest inventory parameters from high-resolution satellite imagery for the Thunkel area, Northern Mongolia. A comparative study on various satellite sensors and data analysis techniques.

With the demise of the Soviet Union and the transition to a market economy starting in the 1990s, Mongolia has been experiencing dramatic changes resulting in social and economic disparities and an increasing strain on its natural resources. The situation is exacerbated by a changing climate, the erosion of forestry related administrative structures, and a lack of law enforcement activities. Mongolia’s forests have been afflicted with a dramatic increase in degradation due to human and natural impacts such as overexploitation and wildfire occurrences. In addition, forest management practices are far from being sustainable. In order to provide useful information on how to viably and effectively utilise the forest resources in the future, the gathering and analysis of forest related data is pivotal. Although a National Forest Inventory was conducted in 2016, very little reliable and scientifically substantiated information exists related to a regional or even local level. This lack of detailed information warranted a study performed in the Thunkel taiga area in 2017 in cooperation with the GIZ. In this context, we hypothesise that (i) tree species and composition can be identified utilising the aerial imagery, (ii) tree height can be extracted from the resulting canopy height model with accuracies commensurate with field survey measurements, and (iii) high-resolution satellite imagery is suitable for the extraction of tree species, the number of trees, and the upscaling of timber volume and basal area based on the spectral properties. The outcomes of this study illustrate quite clearly the potential of employing UAV imagery for tree height extraction (R2 of 0.9) as well as for species and crown diameter determination. However, in a few instances, the visual interpretation of the aerial photographs were determined to be superior to the computer-aided automatic extraction of forest attributes. In addition, imagery from various satellite sensors (e.g. Sentinel-2, RapidEye, WorldView-2) proved to be excellently suited for the delineation of burned areas and the assessment of tree vigour. Furthermore, recently developed sophisticated classifying approaches such as Support Vector Machines and Random Forest appear to be tailored for tree species discrimination (Overall Accuracy of 89%). Object-based classification approaches convey the impression to be highly suitable for very high-resolution imagery, however, at medium scale, pixel-based classifiers outperformed the former. It is also suggested that high radiometric resolution bears the potential to easily compensate for the lack of spatial detectability in the imagery. Quite surprising was the occurrence of dark taiga species in the riparian areas being beyond their natural habitat range. The presented results matrix and the interpretation key have been devised as a decision tool and/or a vademecum for practitioners. In consideration of future projects and to facilitate the improvement of the forest inventory database, the establishment of permanent sampling plots in the Mongolian taigas is strongly advised.2021-06-0

Investigating the Potential of UAV-Based Low-Cost Camera Imagery for Measuring Biophysical Variables in Maize

The potential for improved crop productivity is readily investigated in agronomic field experiments. Frequent measurements of biophysical crop variables are necessary to allow for confident statements on crop performance. Commonly, in-field measurements are tedious, labour-intensive, costly and spatially selective and therefore pose a challenge in field experiments. With the versatile, flexible employment of the platform and the high spatial and temporal resolution of the sensor data, Unmanned Aerial Vehicle (UAV)-based remote sensing offers the possibility to derive variables quickly, contactless and at low cost. This thesis examined if UAV-borne modified low-cost camera imagery allowed for remote estimation of the crop variables green leaf area index (gLAI) and radiation use efficiency (RUE) in a maize field trial under different management influences. For this, a field experiment was established at the university's research station Campus Klein-Altendorf southwest of Bonn in the years 2015 and 2016. In four treatments (two levels of nitrogen fertilisation and two levels of plant density) with five repetitions each, leaf growth of maize plants was supposed to occur differently. gLAI and biomass was measured destructively, UAV-based data was acquired in 14-day intervals over the entire experiment. Three studies were conducted and submitted for peer-review in international journals. In study I, three selected spectral vegetation indices (NDVI, GNDVI, 3BSI) were related to the gLAI measurements. Differing but definite relationships per treatment factor were found. gLAI estimation using the two-band indices (NDVI, GNDVI) yielded good results up to gLAI values of 3. The 3-bands approach (3BSI) did not provide improved accuracies. Comparing gLAI results to the spectral vegetation indices, it was determined that sole reliance on these was insufficient to draw the right conclusions on the impact of management factors on leaf area development in maize canopies. Study II evaluated parametric and non-parametric regression methods on their capability to estimate gLAI in maize, relying on UAV-based low-cost camera imagery with non-plants pixels (i.e. shaded and illuminated soil background) a) included in and b) excluded from the analysis. With regard to the parametric regression methods, all possible band combinations for a selected number of two- and three-band formulations as well as different fitting functions were tested. With regard to non-parametric methods, six regression algorithms (Random Forests Regression, Support Vector Regression, Relevance Vector Machines, Gaussian Process Regression, Kernel Regularized Least Squares, Extreme Learning Machine) were tested. It was found that all non-parametric methods performed better than the parametric methods, and that kernel-based algorithms outperformed the other tested algorithms. Excluding non-plant pixels from the analysis deteriorated models' performances. When using parametric regression methods, signal saturation occurred at gLAI values of about 3, and at values around 4 when employing non-parametric methods. Study III investigated if a) UAV-based low-cost camera imagery allowed estimating RUEs in different experimental plots where maize was cultivated in the growing season of 2016, b) those values were different from the ones previously reported in literature and c) there was a difference between RUEtotal and RUEgreen. Fractional cover and canopy reflectance was determined based on the RS imagery. Our study showed that RUEtotal ranges between 4.05 and 4.59, and RUEgreen between 4.11 and 4.65. These values were higher than those published in other research articles, but not outside the range of plausibility. The difference between RUEtotal and RUEgreen was minimal, possibly due to prolonged canopy greenness induced by the stay-green trait of the cultivar grown. In conclusion, UAV-based low-cost camera imagery allows for estimation of plant variables within a range of limitations

Assessing spring phenology of a temperate woodland : a multiscale comparison of ground, unmanned aerial vehicle and Landsat satellite observations

PhD ThesisVegetation phenology is the study of plant natural life cycle stages. Plant phenological events are related to carbon, energy and water cycles within terrestrial ecosystems, operating from local to global scales. As plant phenology events are highly sensitive to climate fluctuations, the timing of these events has been used as an independent indicator of climate change. The monitoring of forest phenology in a cost-effective manner, at a fine spatial scale and over relatively large areas remains a significant challenge. To address this issue, unmanned aerial vehicles (UAVs) appear to be a potential new platform for forest phenology monitoring. The aim of this research is to assess the potential of UAV data to track the temporal dynamics of spring phenology, from the individual tree to woodland scale, and to cross-compare UAV results against ground and satellite observations, in order to better understand characteristics of UAV data and assess potential for use in validation of satellite-derived phenology. A time series of UAV data were acquired in tandem with an intensive ground campaign during the spring season of 2015, over Hanging Leaves Wood, Northumberland, UK. The radiometric quality of the UAV imagery acquired by two consumer-grade cameras was assessed, in terms of the ability to retrieve reflectance and Normalised Difference Vegetation Index (NDVI), and successfully validated against ground (0.84≤R2≥0.96) and Landsat (0.73≤R2≥0.89) measurements, but only NDVI resulted in stable time series. The start (SOS), middle (MOS) and end (EOS) of spring season dates were estimated at an individual tree-level using UAV time series of NDVI and Green Chromatic Coordinate (GCC), with GCC resulting in a clearer and stronger seasonal signal at a tree crown scale. UAV-derived SOS could be predicted more accurately than MOS and EOS, with an accuracy of less than 1 week for deciduous woodland and within 2 weeks for evergreen. The UAV data were used to map phenological events for individual trees across the whole woodland, demonstrating that contrasting canopy phenological events can occur within the extent of a single Landsat pixel. This accounted for the poor relationships found between UAV- and Landsat-derived phenometrics (R2<0.45) in this study. An opportunity is now available to track very fine scale land surface changes over contiguous vegetation communities, information which could improve characterization of vegetation phenology at multiple scales.The Science without Borders program, managed by CAPES-Brazil (Coordenação de Aperfeiçoamento de Pessoal de Nível Superior)