Color Infrared Research Articles

Usporedba točnosti procjene strukturnih elemenata sastojina fotogrametrijskom izmjerom aerosnimaka različitih prostornih rezolucija

U radu je istražena mogućnost primjene digitalnih aerosnimaka visokih prostornih rezolucija za procjenu glavnih strukturnih elemenata sastojina (srednji sastojinski promjer i visina, broj stabala, temeljnica i volumen). Za dio gospodarske jedinice ‘Donja Kupčina – Pisarovina’ (14 odsjeka) na sistematskom uzorku ploha provedena je manualna (klasična/analogna) stereo fotogrametrijska izmjera na aerosnimkama GSD 30 cm i 10 cm (GSD – eng. Ground Sample Distance). Svakom stablu na plohi određena (interpretirana) je vrsta drveća, izmjerena visina i delineirana krošnja, na temelju čega su procijenjeni glavni strukturni elementi sastojina. Kako bi se utvrdila točnost fotogrametrijski procijenjenih strukturnih elemenata sastojina provedena je usporedba s podacima terenske izmjere. Rezultati provedenog istraživanja potvrdili su velik potencijal primjene digitalnih aerosnimaka visokih prostornih rezolucija u stereomodelu pri inventuri šuma. Usporedbom rezultata fotogrametrijske izmjere na digitalnim aerosnimkama GSD 30 cm i GSD 10 cm s rezultatima terenske izmjere utvrđeno je da aerosnimke GSD 30 cm pružaju mogućnost procjene srednjih sastojinskih visina, sastojinske temeljnice i volumena s gotovo jednakom točnošću kao i aerosnimke GSD 10 cm. Međutim, pogreške procijene srednjeg sastojinskog promjera i broja stabala bile su gotovo dvostruko veće na aerosnimkama GSD 30 cm nego na aerosnimkama GSD 10 cm. Stoga se može zaključiti kako aerosnimke GSD 10 cm sveukupno daju bolje rezultate procjene strukturnih elemenata sastojina, jer prikazuje strukturu sastojine bliže stvarnoj.

An experimental study on thermal and radiative characteristics of natural gas flame in different equivalence ratios by chemiluminescence and IR photography methods

Natural gas, as the cleanest fossil fuel, has been widely applied in central heating systems and industrial burners. From the perspective of energy saving, abundant sources and enormous use of natural gas together with the high costs caused by the widespread use of this fuel make the investigation of natural gas combustion characteristics important. Consequently, in the present work, the thermal and radiative characteristics, and NOx pollutant emission, of a 100,000 kcal/h natural gas burner, widely used in building heating systems, were investigated in the equivalence ratios of 0.42, 0.62, and 0.72. Non-destructive measurements including chemiluminescence method and IR photography were used to investigate the flame characteristics. The results indicated that increasing the equivalence ratio (in the range of 0.42–0.72) moved the peak of flame temperature from flame up-stream to its down-stream and made the flame temperature distribution more uniform. Also it was found that whereas the maximum temperature of flame occurred in small equivalence ratios such as 0.42, the average temperature of flame was 7.8% greater and the average radiation heat flux was 62% greater in large equivalence ratios such as 0.72. The results also showed that increase in equivalence ratio up to 0.72 raised the rate of soot particles generation in the flame and enhanced the average luminosity and radiation of flame in near IR wavelengths and increased the average emissivity coefficient of flame as much as 20%. Moreover, in flame upstream region, the enhancement of radiation heat transfer by increasing equivalence ratio is dominantly as a result of increase in emissivity coefficient of flame, while in flame downstream region, increasing the flame temperature due to gradual combustion will be important in improvement of radiation heat flux.

Application of multisensoral remote sensing data in the mapping of alkaline fens Natura 2000 habitat

The Biebrza River valley (NE Poland) is distinguished by largely intact, highly natural vegetation patterns and very good conservation status of wetland ecosystems. In 2013⿿2014, studies were conducted in the upper Biebrza River basin to develop a remote sensing method for alkaline fen classification ⿿ a protected Natura 2000 habitat (code 7230) ⿿ using remote sensing technologies. High resolution airborne true colour (RGB) and color infrared (CIR) orthophotomaps, the laser scanning point cloud and thermal day and night images were obtained in August 2013 in the Biebrza River basin. At the same time, botanical studies were conducted in this area using conventional phytosociological methods. The random forest classification method was used to distinguish patches (phytocoenoses) of alkaline fens in the study area. The developed method of wetland identification has an accuracy of 91.5%. The night land surface temperature (LST) appears to have the greatest indicator potential. The obtained inventory results were compared with the results of the traditional habitat 7230 mapping method, carried out in 2011⿿2013 by another team of authors, under the Management Plan (MP, 2014). The obtained results suggest that the developed method has a wide application in nature conservation. Remote sensing methods are alternatives to traditional methods and can be used to identify alkaline fens on a larger scale.

Airborne laser altimetry and multispectral imagery for modeling Golden‐cheeked Warbler (Setophaga chrysoparia) density

AbstractRobust models of wildlife population size, spatial distribution, and habitat relationships are needed to more effectively monitor endangered species and prioritize habitat conservation efforts. Remotely sensed data such as airborne laser altimetry (LiDAR) and digital color infrared (CIR) aerial photography combined with well‐designed field studies can help fill these information voids. We used point count‐based distance sampling survey data and LiDAR‐fused CIR aerial photography to model density of the Golden‐cheeked Warbler (Setophaga chrysoparia), an endangered songbird, on the 10 000‐ha Balcones Canyonlands National Wildlife Refuge (BCNWR). We developed a novel set of candidate models to explain Golden‐cheeked Warbler detection probability and density using habitat covariates characterizing vegetation structure, composition, and complexity as well as habitat fragmentation, topography, and human infrastructure. We had the most model support for covariates calculated using focal means representing a 3.2 ha territory size (100 m radius) vs. 1.8 and 7.0 ha territory sizes. Detection probability decreased with canopy cover and increased with topographic roughness. Golden‐cheeked Warbler density increased with canopy cover, was highest at a 7:3 ratio of Ashe juniper (Juniperus ashei) to broadleaf tree canopy cover, and decreased with global solar radiation. Predicted warbler densities using 3 min point counts were similar to six estimates from independently collected warbler territory mapping on BCNWR with a mean difference of 6% and a Root Mean Squared Error of 1.88 males/40 ha. The total population size for BCNWR was estimated at 884 Golden‐cheeked Warbler males (95% CI 662, 1206) and predicted densities across the refuge ranged from 0.0 to 0.50 male warblers per ha. On the basis of observed habitat relationships, we defined high quality habitat as having at least 60% canopy cover with Ashe juniper comprising 50–90% of the canopy. We estimated 48% of the area at BCNWR managed for Golden‐cheeked Warblers was in high quality habitat conditions and identified patches within the lower habitat quality areas (14% of warbler management areas) that had the greatest potential to become high quality habitat with management. Our approach combined robust wildlife surveys with highly scalable remotely sensed data to examine habitat relationships, estimate population size, and identify existing areas of high quality habitat. This method can be applied to other species of conservation interest and can be used with multiple years of remotely sensed data to assess changes in habitat at local to regional scales.

High-Resolution Classification of South Patagonian Peat Bog Microforms Reveals Potential Gaps in Up-Scaled CH4 Fluxes by use of Unmanned Aerial System (UAS) and CIR Imagery

South Patagonian peat bogs are little studied sources of methane (CH4). Since CH4 fluxes can vary greatly on a small scale of meters, high-quality maps are needed to accurately quantify CH4 fluxes from bogs. We used high-resolution color infrared (CIR) images captured by an Unmanned Aerial System (UAS) to investigate potential uncertainties in total ecosystem CH4 fluxes introduced by the classification of the surface area. An object-based approach was used to classify vegetation both on species and microform level. We achieved an overall Kappa Index of Agreement (KIA) of 0.90 for the species- and 0.83 for the microform-level classification, respectively. CH4 fluxes were determined by closed chamber measurements on four predominant microforms of the studied bog. Both classification approaches were employed to up-scale CH4 closed chamber measurements in a total area of around 1.8 hectares. Including proportions of the surface area where no chamber measurements were conducted, we estimated a potential uncertainty in ecosystem CH4 fluxes introduced by the classification of the surface area. This potential uncertainty ranged from 14.2 mg·m−2·day−1 to 26.8 mg·m−2·day−1. Our results show that a simple classification with only few classes potentially leads to pronounced bias in total ecosystem CH4 fluxes when plot-scale fluxes are up-scaled.

Quality assessment of high density digital surface model over different land cover classes

Background and Purpose: Recent research on generation of digital surface models (DSMs) using image matching methods revealed a great potential of DSM application in forestry, especially in forest inventory. However, research dealing with DSM generation from digital aerial images are still lacking in Croatia. Therefore, the main objective of this study was to present the workflow for generating high density DSM from colour infrared (CIR) digital stereo aerial images using area-based image matching algorithm. Materials and Methods: The high density DSM was generated from colour infrared digital aerial stereo images using Dense DTM algorithm of PHOTOMOD software - an area-based image matching algorithm which operates on the principle of cross-correlation approach. To evaluate the quality of the generated DSM, an agreement assessment with manual stereo measurements was conducted over three different land cover classes (forests, shrubs, grasslands) using the same images as for DSM generation. Results: The good vertical agreement between the generated DSM and stereo measurement was achieved for all three land cover classes present at the research area. The highest vertical agreement was obtained for the grassland land cover class (RMSE=0.36), slightly lower for forest (RMSE=0.62), whereas the lowest vertical agreement was obtained for shrub land cover class (RMSE=0.83). Conclusions: The results of this research are very promising and suggest that the high density DSM generated from digital aerial stereo images and by using the proposed methodology has the potential to be used in forestry, primarily in forest inventory. Therefore, further research should be focused on generation of CHM by subtracting available DTM from the high density DSM and on the examination of its potential for deriving various forest attributes.

Quality assessment of high density digital surface model over different land cover classes

Background and Purpose: Recent research on generation of digital surface models (DSMs) using image matching methods revealed a great potential of DSM application in forestry, especially in forest inventory. However, research dealing with DSM generation from digital aerial images are still lacking in Croatia. Therefore, the main objective of this study was to present the workflow for generating high density DSM from colour infrared (CIR) digital stereo aerial images using area-based image matching algorithm. Materials and Methods: The high density DSM was generated from colour infrared digital aerial stereo images using Dense DTM algorithm of PHOTOMOD software - an area-based image matching algorithm which operates on the principle of cross-correlation approach. To evaluate the quality of the generated DSM, an agreement assessment with manual stereo measurements was conducted over three different land cover classes (forests, shrubs, grasslands) using the same images as for DSM generation. Results: The good vertical agreement between the generated DSM and stereo measurement was achieved for all three land cover classes present at the research area. The highest vertical agreement was obtained for the grassland land cover class (RMSE=0.36), slightly lower for forest (RMSE=0.62), whereas the lowest vertical agreement was obtained for shrub land cover class (RMSE=0.83). Conclusions: The results of this research are very promising and suggest that the high density DSM generated from digital aerial stereo images and by using the proposed methodology has the potential to be used in forestry, primarily in forest inventory. Therefore, further research should be focused on generation of CHM by subtracting available DTM from the high density DSM and on the examination of its potential for deriving various forest attributes.

Examination of Surface Temperature Modification by Open-Top Chambers along Moisture and Latitudinal Gradients in Arctic Alaska Using Thermal Infrared Photography

Passive warming manipulation methodologies, such as open-top chambers (OTCs), are a meaningful approach for interpretation of impacts of climate change on the Arctic tundra biome. The magnitude of OTC warming has been studied extensively, revealing an average plot-level warming of air temperature that ranges between 1 and 3 °C as measured by shielded resistive sensors or thermocouples. Studies have also shown that the amount of OTC warming depends in part on location climate, vegetation, and soil properties. While digital infrared thermometers have been employed in a few comparisons, most of the focus of the effectiveness of OTC warming has been on air or soil temperature rather than tissue or surface temperatures, which directly translate to metabolism. Here we used thermal infrared (TIR) photography to quantify tissue and surface temperatures and their spatial variability at a previously unavailable resolution (3–6 mm2). We analyzed plots at three locations that are part of the International Tundra Experiment (ITEX)-Arctic Observing Network (AON-ITEX) network along both moisture and latitudinal gradients spanning from the High Arctic (Barrow, AK, USA) to the Low Arctic (Toolik Lake, AK, USA). Our results show a range of OTC surface warming from 2.65 to 1.27 °C (31%–10%) at our three sites. The magnitude of surface warming detected by TIR imagery in this study was comparable to increases in air temperatures previously reported for these sites. However, the thermal images revealed wide ranges of surface temperatures within the OTCs, with some surfaces well above ambient unevenly distributed within the plots under sunny conditions. We note that analyzing radiometric temperature may be an alternative for future studies that examine data acquired at the same time of day from sites that are in close geographic proximity to avoid the requirement of emissivity or atmospheric correction for validation of results. We foresee future studies using TIR photography to describe species-level thermodynamics that could prove highly valuable toward a better understanding of species-specific responses to climate change in the Arctic.

A Comparative Exploration of Enhancing Thermal Characteristics of Natural Gas Flame by Synchronous Combustion Technique

This article is a comparative study of how the injection of micro kerosene droplets and pulverized anthracite coal particles affects soot particle nucleation inside natural gas flame and, subsequently, radiation. To this end, the yellow chemiluminescence of soot particles and IR photography were used to locate radiative soot particles and discover their qualitative distribution. The IR filter was tested with a Thermo Nicolet Avatar 370 FTIR Spectrometer for its spectral transmittance to be specified. Also, the spectral absorbance of soot particles, which are formed in flame, was measured by BOMEM FTIR. Furthermore, the variations of flame temperature, transient heat transfer, and thermal efficiency were investigated. The results indicate that, for equal heating values, kerosene droplets are more effective than coal particles in improving the radiation and thermal characteristics of natural gas flame. Also, kerosene droplets cause a higher rise in the temperature in flame downstream and make the axial flame temperature more uniform than coal particles do. In quantitative terms, when kerosene droplets were injected, the radiative heat transfer and thermal efficiency of flame were 93% and 35% higher than the corresponding values for the coal particles injection mode.

Development of a versatile XRF scanner for the elemental imaging of paintworks

Scanning XRF is a powerful elemental imaging technique introduced at the synchrotron that has recently been transposed to laboratory. The growing interest in this technique stems from its ability to collect images reflecting pigment distribution within large areas on artworks by means of their elemental signature. In that sense, scanning XRF appears highly complementary to standard imaging techniques (Visible, UV, IR photography and X-ray radiography). The versatile XRF scanner presented here has been designed and built at the C2RMF in response to specific constraints: transportability, cost-effectiveness and ability to scan large areas within a single working day. The instrument is based on a standard X-ray generator with sub-millimetre collimated beam and a SDD-based spectrometer to collected X-ray spectra. The instrument head is scanned in front of the painting by means of motorised movements to cover an area up to 300 × 300 mm2 with a resolution of 0.5 mm (600 × 600 pixels). The 15-kg head is mounted on a stable photo stand for rapid positioning on paintworks and maintains a free side-access for safety; it can also be attached to a lighter tripod for field measurements. Alignment is achieved with a laser pointer and a micro-camera. With a scanning speed of 5 mm/s and 0.1 s/point, elemental maps are collected in 10 h, i.e. a working day. The X-ray spectra of all pixels are rapidly processed using an open source program to derive elemental maps. To illustrate the capabilities of this instrument, this contribution presents the results obtained on the Belle Ferronniere painted by Leonardo da Vinci (1452–1519) and conserved in the Musee du Louvre, prior to its restoration at the C2RMF.

Are vegetation indices derived from consumer-grade cameras mounted on UAVs sufficiently reliable for assessing experimental plots?

Abstract Recent technological advances in UAV (unmanned aerial vehicle) technology offer new opportunities for assessing agricultural plot experiments using UAV imagery. Vegetation indices (VIs) based on aerial images derived from consumer-grade cameras are a simple and cheap alternative compared to VIs derived from proximal (on-ground) sensors. The objective of this study was to assess whether VIs derived from consumer-grade cameras mounted on UAVs are reliable and whether there are any shortcomings in image acquisition and analysis that need to be addressed before their general application. This objective was investigated using a rotary-wing and a fixed-wing UAV, true colour (RGB) and colour-infrared (CIR) cameras, four different VIs (ExG, NGRDI, NDVI and ENDVI), altitudes in the range of 30–100 m, different ambient lighting conditions and two different software packages for stitching images together. Results were compared with ground-based recordings by consumer-grade cameras and multispectral sensors. Field experiments in cereals were used to evaluate the assessments. The study showed that VIs based on UAV imagery have the same ability to quantify crop responses to experimental treatments as ground-based recordings with cameras and advanced sensors. However, there are shortcomings that need to be taken into consideration: (1) angular variation in reflectance (bidirectional reflectance), (2) stitching and (3) ambient light fluctuations. Bidirectional reflectance was so extensive that it could lead to misleading conclusions in sunny conditions and this effect could be amplified further by stitching. A procedure for avoiding impacts from bidirectional reflectance is demonstrated when plots were cropped from individual images and a procedure is suggested for stitching images. Camera, VIs and image acquisition altitude were of minor importance, but fluctuating ambient lighting conditions is an issue that should be addressed in future studies.

Multi-tier method using infrared photography and GPR to detect and locate water leaks

Abstract This paper presents newly developed method for detecting and locating leaks in water distribution networks utilizing two detection techniques; ground penetrating radar (GPR) and infrared photography (IR). The experimental work and field investigation were carried out over 2 years in three locations in City of Doha, Qatar to capture 115 IR image frames and 23 GPR image frames. Firstly, GPR technology is utilized to accurately define location of buried pipes. After locating these pipes, IR images are collected for simulated and actual leaks. The developed algorithm segments each image into leakage and non-leakage areas and the centroid of each leakage is calculated using Green's theorem. Subsequently, GPR images are introduced as a second layer and overlaid with IR images to compare pipes location with leak location. The method was successfully applied to detect simulated and actual leaks in summer and winter seasons with small margin of error (2.9–5.6%) in estimating leakage areas. When examining the investigated four operating conditions, it was found that the developed method can predict leaks in a more reliable way if the camera height is 2 m and the speed is 1.65 m/s in both simulated and actual leaks. The newly developed method is robust and can aid operators and city engineers in detecting and locating water leaks with high accuracy.

Combining fuzzy set theory and nonlinear stretching enhancement for unsupervised classification of cotton root rot

Cotton root rot is a destructive disease affecting cotton production. Accurate identification of infected areas within fields is useful for cost-effective control of the disease. The uncertainties caused by various infection stages and newly infected plants make it difficult to achieve accurate classification results from airborne imagery. The objectives of this study were to apply fuzzy set theory and nonlinear stretching enhancement to airborne multispectral imagery for unsupervised classification of cotton root rot infections. Four cotton fields near Edroy and San Angelo, Texas, were selected for this study. Airborne multispectral imagery was taken and the color-infrared (CIR) composite images were used for classification. The intensity component was enhanced by using a fuzzy-set based method, and the saturation component was enhanced by a nonlinear stretching image enhancement algorithm. The enhanced CIR composite images were then classified into infected and noninfected areas. Iterative self organization data analysis and adaptive Otsu’s method were used to compare the performance of the proposed image enhancement method. The results showed that image enhancement has improved the classification accuracy of these two unsupervised classification methods for all four fields. The results from this study will be useful for detection of cotton root rot and for site-specific treatment of the disease.

AUTOMATIC DETECTION OF BUILDING POINTS FROM LIDAR AND DENSE IMAGE MATCHING POINT CLOUDS

Abstract. This study aims to detect automatically building points: (a) from LIDAR point cloud using simple techniques of filtering that enhance the geometric properties of each point, and (b) from a point cloud which is extracted applying dense image matching at high resolution colour-infrared (CIR) digital aerial imagery using the stereo method semi-global matching (SGM). At first step, the removal of the vegetation is carried out. At the LIDAR point cloud, two different methods are implemented and evaluated using initially the normals and the roughness values afterwards: (1) the proposed scan line smooth filtering and a thresholding process, and (2) a bilateral filtering and a thresholding process. For the case of the CIR point cloud, a variation of the normalized differential vegetation index (NDVI) is computed for the same purpose. Afterwards, the bare-earth is extracted using a morphological operator and removed from the rest scene so as to maintain the buildings points. The results of the extracted buildings applying each approach at an urban area in northern Greece are evaluated using an existing orthoimage as reference; also, the results are compared with the corresponding classified buildings extracted from two commercial software. Finally, in order to verify the utility and functionality of the extracted buildings points that achieved the best accuracy, the 3D models in terms of Level of Detail 1 (LoD 1) and a 3D building change detection process are indicatively performed on a sub-region of the overall scene.

The Importance of CIR Aerial Imagery in Inventory, Monitoring and Predicting Forest Condition

Background and Purpose: The main goal of this paper was to highlight the importance of colour infrared (CIR) aerial photographs for efficient inventory, monitoring and predicting the health status of forests in changed site conditions. CIR aerial photographs from two aerial surveys conducted in 1989 and 2008 were used to identify and analyze the damage in lowland pedunculated oak (Quercus robur L.) forests during each period, as well as to obtain a dieback trend in the observed period. Material and Methods: The research was conducted in lowland pedunculated oak forests of Josip Kozarac management unit. CIR aerial photographs (1989) of the research area were taken with a classical camera, while aerial images in 2008 were taken with a digital camera and then converted from digital to analogue form (contact copy - photograph) in order to perform photointerpretation with a SOKKIA MS27 Carl ZEISS Jena mirror stereoscope, magnified by 8x. The health status of particular trees (crowns) was assessed by means of photointerpretation keys in a stereomodel over a systematic 100x100 m sample grid on both 1989 and 2008 aerial photographs. The degree of damage of 4 individual trees was assessed at every grid point in the surveying strips covering the surveyed area. Damage indicators were calculated and thematic maps were constructed on the basis of the interpretation of data for all the grid points. Results: For the research area a damage index (IO) of 68.36% for oak was determined by photointerpreting individual trees (2008) ; in other words, this percentage of pedunculate oak trees in the surveyed area was found to be in the damage degree of 2.1 and more. Of 68.36% trees classified in the damage degree of 2.1 or more, mean damage (SO1) amounted to 52.16% and could be classified in the damage degree of 2.2. In 1989, the mean damage index (IO) for pedunculate oak was 48.00%, and pedunculate oak trees with mean damage degree of 2.1 or more (SO1) amounted to 36.03%. The comparison of photointerpretation results from 1989 and 2008 shows that the damage index (IO) for pedunculate oak increased by more than 20%, whereas mean damage (SO) rose by almost 15%. An increase of more than 15% in mean damage of considerably damaged pedunculate oak trees can also be observed. Conclusions: The obtained results provide not only the current forest condition but also indicate the condition in the field and the purposefulness of aerial surveying, since aerial photographs allow an insight into the condition in the field in a short time period. This in turn enables the efficient application of measures to mitigate the consequences of dieback.

Making the invisible visible: using UAS-based high-resolution color-infrared imagery to identify buried medieval monastery walls

Remote sensing techniques have become an increasingly important tool for surveying archaeological sites. However, budgeting issues in archaeological research often limit the application of satellite or airborne imagery. Unmanned aerial systems (UAS) provide a flexible, quick, and more economical alternative to commonly used remote sensing techniques. In this study, the buried features of the archaeological site of the Kleinburlo monastery, near Münster, Germany, were identified using high-resolution color–infrared (CIR) images collected from a UAS platform. Based on these CIR images, a modified normalised difference vegetation index (NDVIblue) was calculated, showing reflectance spectra of vegetation anomalies caused by water stress. In the presented study, the vegetation growing on top of the buried walls was better nourished than the surrounding plants because very wet conditions over the days previous to data collection caused higher levels of water stress in the surrounding water-drenched land. This difference in water stress was a good indicator for detecting archaeological remains.

A Simplified Empirical Line Method of Radiometric Calibration for Small Unmanned Aircraft Systems-Based Remote Sensing

The use of small unmanned aircraft systems (sUAS) to acquire very high-resolution multispectral imagery has attracted growing attention recently; however, no systematic, feasible, and convenient radiometric calibration method has been specifically developed for sUAS remote sensing. In this research, we used a modified color infrared (CIR) digital single-lens reflex (DSLR) camera as the sensor and the DJI S800 hexacopter sUAS as the platform to collect imagery. Results show that the relationship between the natural logarithm of measured surface reflectance and image raw, unprocessed digital numbers (DNs) is linear and the y-intercept of the linear equation can be theoretically interpreted as the minimal possible surface reflectance that can be detected by each sensor waveband. The empirical line calibration equation for every single band image can be built using the y-intercept as one data point, and the natural log-transformed measured reflectance and image DNs of a gray calibration target as another point in the coordinate system. Image raw DNs are therefore converted to reflectance using the calibration equation. The Mann-Whitney U test results suggest that the difference between the measured and the predicted reflectance values of 13 tallgrass sampling quadrats is not statistically significant. The method theory developed in this study can be employed for other sUAS-based remote sensing applications.

Analysis of Unmanned Aerial System-Based CIR Images in Forestry—A New Perspective to Monitor Pest Infestation Levels

The detection of pest infestation is an important aspect of forest management. In the case of the oak splendour beetle (Agrilus biguttatus) infestation, the affected oaks (Quercus sp.) show high levels of defoliation and altered canopy reflection signature. These critical features can be identified in high-resolution colour infrared (CIR) images of the tree crown and branches level captured by Unmanned Aerial Systems (UAS). In this study, we used a small UAS equipped with a compact digital camera which has been calibrated and modified to record not only the visual but also the near infrared reflection (NIR) of possibly infested oaks. The flight campaigns were realized in August 2013, covering two study sites which are located in a rural area in western Germany. Both locations represent small-scale, privately managed commercial forests in which oaks are economically valuable species. Our workflow includes the CIR/NIR image acquisition, mosaicking, georeferencing and pixel-based image enhancement followed by object-based image classification techniques. A modified Normalized Difference Vegetation Index (NDVImod) derived classification was used to distinguish between five vegetation health classes, i.e., infested, healthy or dead branches, other vegetation and canopy gaps. We achieved an overall Kappa Index of Agreement (KIA) of 0.81 and 0.77 for each study site, respectively. This approach offers a low-cost alternative to private forest owners who pursue a sustainable management strategy.

Investigating Correlation among NDVI Index Derived by Unmanned Aerial Vehicle Photography and Grain Yield under Late Drought Stress Conditions

Normalized Difference Vegetation Index (NDVI) has been related with grain yield and other important agronomic traits of rainfed cereal crops grown under Mediterranean conditions. In most studies, spectral measurements were carried out using portable spectroradiometers; however, Unmanned Aerial Vehicles (UAVs) carrying conventional digital cameras equipped with spectral filters have been recently used as an alternative. The main scope of this work was to investigate whether grain yield and yield-related traits under late drought stress conditions are correlated with NDVI Index derived by low height combined photography obtained by UAVs.Experiments were sown at the Athalassa experimental station for two consecutive growing seasons. Twenty durum wheat varieties were sown in a Randomized Complete Block Design with four and six replications in the 1st and 2nd growing season, respectively. In the 1st growing season, plants received 270mm of rainfall and extra 30mm irrigation during booting. In the 2nd growing season, plants received 140mm of rainfall, 50mm irrigation during tillering and 50mm irrigation during booting. In both growing seasons, plants received no rainfall or irrigation from booting to maturity.Conventional and near infra red photography were taken by a CanonIS200 camera mounted on a UAV. Geo-processing of the imagery and orthorectification was applied on the data to become compatible. After that proper samples were masked from the orthoimagery and processed for NDVI index calculation. Two flights were carried out during the 1st growing season when most varieties were at booting and milk stage respectively. Four flights were carried out during the 2nd growing season when most varieties were at booting, heading, milk and dough stages respectively.In our study there was significant correlation between yield and NDVI in both growing seasons with the correlation been stronger during the 2nd season. Correlations were stronger when estimated during the milk and dough stages.

A Novel Methodology to Estimate Single-Tree Biophysical Parameters from 3D Digital Imagery Compared to Aerial Laser Scanner Data

Airborne laser scanner (ALS) data provide an enhanced capability to remotely map two key variables in forestry: leaf area index (LAI) and tree height (H). Nevertheless, the cost, complexity and accessibility of this technology are not yet suited for meeting the broad demands required for estimating and frequently updating forest data. Here we demonstrate the capability of alternative solutions based on the use of low-cost color infrared (CIR) cameras to estimate tree-level parameters, providing a cost-effective solution for forest inventories. ALS data were acquired with a Leica ALS60 laser scanner and digital aerial imagery (DAI) was acquired with a consumer-grade camera modified for color infrared detection and synchronized with a GPS unit. In this paper we evaluate the generation of a DAI-based canopy height model (CHM) from imagery obtained with low-cost CIR cameras using structure from motion (SfM) and spatial interpolation methods in the context of a complex canopy, as in forestry. Metrics were calculated from the DAI-based CHM and the DAI-based Normalized Difference Vegetation Index (NDVI) for the estimation of tree height and LAI, respectively. Results were compared with the models estimated from ALS point cloud metrics. Field measurements of tree height and effective leaf area index (LAIe) were acquired from a total of 200 and 26 trees, respectively. Comparable accuracies were obtained in the tree height and LAI estimations using ALS and DAI data independently. Tree height estimated from DAI-based metrics (Percentile 90 (P90) and minimum height (MinH)) yielded a coefficient of determination (R2) = 0.71 and a root mean square error (RMSE) = 0.71 m while models derived from ALS-based metrics (P90) yielded an R2 = 0.80 and an RMSE = 0.55 m. The estimation of LAI from DAI-based NDVI using Percentile 99 (P99) yielded an R2 = 0.62 and an RMSE = 0.17 m2/m−2. A comparative analysis of LAI estimation using ALS-based metrics (laser penetration index (LPI), interquartile distance (IQ), and Percentile 30 (P30)) yielded an R2 = 0.75 and an RMSE = 0.14 m2/m−2. The results provide insight on the appropriateness of using cost-effective 3D photo-reconstruction methods for targeting single trees with irregular and heterogeneous tree crowns in complex open-canopy forests. It quantitatively demonstrates that low-cost CIR cameras can be used to estimate both single-tree height and LAI in forest inventories.