Color Infrared Research Articles

Observations on groundwater‐surface water interactions at River Vantaa, Finland

PurposeThe purpose of this paper is to emphasize the importance of groundwater‐surface water interaction when studying, modeling and assessing climate change impacts on river water management.Design/methodology/approachThe investigations were focused on River Vantaa and its tributaries in southern Finland. The main methods used involved aerial infrared photography, thermal profiling of river sediments, water quality measurements, isotopic composition of oxygen and hydrogen δ18O, δ2H and river water temperature measurements. The authors present the first results of the field measurements targeted to identify the groundwater recharge and discharge zones within the river system.FindingsGroundwater discharge zones were found to have a significant impact on water quality and volume in River Vantaa and its tributaries. In the drainage basin, the aerial infrared photography seemed to be a feasible and cost‐effective method to identify areas of groundwater discharge across the entire river basin. Around 350 groundwater/surface water interaction sites along the 220 km river system could be identified.Practical implicationsThe interaction sites identified during the season of low flow rate should be considered as potential risk areas because during flood periods groundwater quality might be at risk due to bank infiltration. This should be considered in river basin management within predicted changing climatic conditions.Originality/valueThis is the first attempt in Finland to map systematically groundwater and river water interactions. The focus of the paper is relevant, because according to the existing climate scenarios, flooding of the main rivers in Finland will be more frequent in future, increasing the probability of groundwater‐surface water interaction.

301 GPS誘導農地赤外線航空撮影システムの開発(流体工学I)

301 GPS誘導農地赤外線航空撮影システムの開発(流体工学I)

SPATIAL DEPENDENCE OF RANDOM SETS AND ITS APPLICATION TO DISPERSION OF BARK BEETLE INFESTATION IN A NATURAL FOREST

A large spatio-temporal data set monitoring the annual progress of bark beetle infestation in the Bavarian Forest National Park (Germany) is statistically analysed by means of complex image analysis algorithms. The infestation data were obtained by color-infrared (CIR) aerial image interpretation and cover 10 subsequent years (2001–2010). Newly emerged infestation patches are hypothesized as spatially correlated to locations of previous year’s infestation. Both areas, source patches and subsequently emerged patches, are considered as two disjoint random sets. Their spatio-temporal dependence is analysed by two methods: the classical approach based on the measurement of cross-covariance functions, and a second one based on nearest neighbor distances. The resulting characteristics can be interpreted as pre-disposition probabilities of bark beetle infestation depending on distance to sources. Both methods show a strong short-range preference, which decreases with increasing distances.

Latent Evidence Detection using a Combination of Near Infrared and High Dynamic Range Photography: An Example Using Bloodstains

In this paper, we use bloodstains to illustrate an approach for identifying latent evidence on dark cloth using near infrared (NIR) photography combined with high dynamic range (HDR) photography techniques. NIR photography alone has been used to capture latent evidence that cannot be seen in normal ambient light. HDR techniques combine multiple bracketed photographs of the same image to increase the dynamic range of the photograph which can provide greater contrast. Using NIR photography alone, we were able to detect a bloodstain up to a 1/16 dilution, an improvement over previous studies. Combining NIR photography with the HDR process resulted in a noticeable increase in visibility up to 1/16 dilution when compared to NIR photographs alone. At 1/32 dilution, we were able to detect bloodstains that were not visible using NIR alone. NIR is a useful tool for imaging latent evidence, and combining NIR with HDR consistently provides better results over NIR alone.

Large-scale leaf area index inversion algorithms from high-resolution airborne imagery

Large-scale leaf area index (LAI) inversion algorithms were developed to determine the LAI of a forest located in Gatineau Park, Canada, using high-resolution colour and colour infrared (CIR) digital airborne imagery. The algorithms are parameter-independent and developed based on the principles of optical field instruments for gap fraction measurements. Cloud-free colour and CIR images were acquired on 21 August 2007 with 35 and 60 cm nominal ground pixel size, respectively. Normalized Difference Vegetation Index (NDVI), maximum likelihood and object-oriented classifications, and principal component analysis (PCA) methods were applied to calculate the mono-directional gap fraction. Subsequently, LAI was derived from inversion and compared with ground measurements made in 54 plots of 20 by 20 m using hemispherical photography between 10 and 20 August 2007. There was high inter-correlation (the Pearson correlation coefficient, R > 0.5, p < 0.01) among LAI values inverted using the classifications and PCA methods, but neither were highly correlated with LAI inverted from the NDVI method. LAI inverted from the NDVI-based gap fraction significantly correlated with ground-measured LAI (R = 0.63, root mean square error (RMSE) = 0.52), while LAI inverted from the classification and PCA-derived gap fraction showed poor correlation with ground-measured LAI. Consequently, the NDVI method was used to invert LAI for the whole study area and produce a 20‐m resolution LAI map.

DETECTION OF SALT TOXICITY AND NITROGEN DEFICIENCY IN CUCUMIS SATIVUS L. USING SPECTRORADIOMETRY AND COLOR INFRARED IMAGERY

Spectral changes in Cucumis sativus L. plants caused by sodium chloride (NaCl) toxicity and nitrogen deficiency were evaluated to determine if abiotic stressors were detectable using color infrared (CIR) imagery. Sodium chloride toxicity was manifested by a decrease in reflectance of green and near-infrared (NIR) wavelengths (550 and 825 nm, respectively), which became evident one week post-treatment among plants subjected to high salt concentrations (0.10 and 0.20 M NaCl). The latter exhibited an increase in reflectance of red wavelengths (680 nm), although effects did not become pronounced until the third week post-treatment. In contrast, spectral changes associated with nitrogen deficiency were the most pronounced in the visible wavelengths and varied in the time required for symptom manifestation. 100% nitrogen (N) deficiency symptoms became evident two weeks before those in plants with 50% nitrogen deficiency. Spectral differences were also detectable in CIR images acquired under artificial lighting. Subtle differences among treatments were accentuated using ratio images (NIR/red).

Object-Based Image Analysis for Detection of Japanese Knotweed s.l. taxa (Polygonaceae) in Wales (UK)

Japanese Knotweed s.l. taxa are amongst the most aggressive vascular plant Invasive Alien Species (IAS) in the world. These taxa form dense, suppressive monocultures and are persistent, pervasive invaders throughout the more economically developed countries (MEDCs) of the world. The current paper utilises the Object-Based Image Analysis (OBIA) approach of Definiens Imaging Developer software, in combination with very high spatial resolution (VHSR) colour infra-red (CIR) and visible‑band (RGB) aerial photography in order to detect Japanese Knotweed s.l. taxa in Wales (UK). An algorithm was created using Definiens in order to detect these taxa, using variables found to effectively distinguish them from landscape and vegetation features. The results of the detection algorithm were accurate, as confirmed by field validation and desk‑based studies. Further, these results may be incorporated into Geographical Information Systems (GIS) research as they are readily transferable as vector polygons (shapefiles). The successful detection results developed within the Definiens software should enable greater management and control efficacy. Further to this, the basic principles of the detection process could enable detection of these taxa worldwide, given the (relatively) limited technical requirements necessary to conduct further analyses.

Comparison of hyperspectral imagery with aerial photography and multispectral imagery for mapping broom snakeweed

Broom snakeweed (Gutierrezia sarothrae (Pursh) Britt. & Rusby) is one of the most widespread and abundant rangeland weeds in western North America. The objectives of this study were to evaluate airborne hyperspectral imagery and compare it with aerial colour-infrared (CIR) photography and multispectral digital imagery for mapping broom snakeweed infestations. Airborne hyperspectral imagery along with aerial CIR photographs and digital CIR images was acquired from a rangeland area in south Texas. The hyperspectral imagery was transformed using minimum noise fraction (MNF) and then classified using minimum distance, Mahalanobis distance, maximum likelihood, and spectral angle mapper (SAM) classifiers. The digitized aerial photographs and the digital images were respectively mosaicked as one photographic image and one digital image; these were then classified using the same classifiers. Accuracy assessment showed that the maximum likelihood classifier performed the best for the three types of images. The best overall accuracies for three-class classification maps (snakeweed, mixed woody and mixed herbaceous) were 91.0%, 92.5%, and 95.0%, respectively, for the CIR photographic image, the digital CIR image and the MNF-transformed hyperspectral image. Kappa analysis showed that there were no significant differences in maximum likelihood-based classifications among the three types of images. These results indicate that airborne hyperspectral imagery along with aerial photography and multispectral imagery can be used for monitoring and mapping broom snakeweed infestations on rangelands.

Integration of varying spatial, spectral and temporal high-resolution optical images for individual tree crown isolation

Automated methods for capturing geometric and spectral properties of individual tree crowns are becoming increasingly viable options for use in natural resource planning. Crown isolation techniques are needed that are capable of adapting to the changing availability and resolutions of remotely sensed data. Data integration, or the fusion of two distinct data entities, offers a methodological framework that can compensate for the shortcomings of individual datasets while enhancing their desirable features. This study sought to develop a method of data integration for high-resolution optical images of varying spatial and temporal resolutions to improve the automatic detection and delineation of individual tree crowns. A marker-controlled watershed segmentation (MCWS) algorithm was developed for a 30-cm-per-pixel-side airborne colour infrared (CIR) digital image of a leaf-on apple (Malus spp.) orchard. Three methods of obtaining the markers needed for the MCWS algorithm were tested: (1) manual marker selection, (2) template/correlation selection using the 30-cm CIR image, and (3) template/correlation selection using a 15-cm-per-pixel-side true (TRU) colour leaf-off aerial image. The effectiveness of integrating marker data of different temporal and spatial resolutions with the segmentation process of the CIR image scene was tested. A comparison of crown isolation results using markers derived within the segmented 30-cm CIR digital imagery with results from markers derived from the 15-cm TRU image scene indicated greater accuracies to detect and isolate tree crowns with data integration.

Use of Archive Aerial Photography for Monitoring Black Mangrove Populations

A study was conducted on the South Texas Gulf Coast to evaluate archive aerial color-infrared (CIR) photography combined with supervised image analysis techniques to quantify changes in black mangrove [Avicennia germinans (L.) L.] populations over a 26-year period. Archive CIR film from two study sites (sites 1 and 2) was studied. Photographs of site 1 from 1976, 1988, and 2002 showed that black mangrove populations made up 16.2%, 21.1%, and 29.4% of the study site, respectively. Photographs of site 2 from 1976 and 2002 showed that black mangrove populations made up 0.4% and 2.7% of the study site, respectively. Over the 26-year period, black mangrove had increases in cover of 77% and 467% on sites 1 and 2, respectively. These results indicate that aerial photographs coupled with image analysis techniques can be useful tools to monitor and quantify black mangrove populations over time.

Delineating new foot trails within the US-Mexico border zone using semi-automatic linear object extraction methods and very high resolution imagery

A consequence of illegal immigration and border law enforcement practices has been the development of a complex foot trail network in remote areas of San Diego County near the US–Mexico border. Comprehensive monitoring of changes in trail networks requires multitemporal remote sensing analyses. Three semi-automated, commercially available object extraction routines were evaluated for delineating new trails. Three types of multi-temporal image products were generated from two dates of scanned colour infrared (CIR) images of two US–Mexico border study sites, at four spatial resolutions (15, 30, 60 and 120 cm). Accuracy was assessed using reference data created by manual image interpretation. The semi-automated routines captured most of the new trail objects, but also contained gaps and high commission error. Products derived using a spatial contextual-based neural network classifier with CIR image difference or red band layer stack and 15 or 30 cm spatial resolution image inputs yielded the best extractions of new trails.

Automated Detection and Location of Leaks in Water Mains Using Infrared Photography

Leakage of water distribution networks is the most common reason of undesirable losses of potable water. Problems associated with water main leaks pose growing concern around the globe. These problems include water and energy loss, in addition to the risk it poses to structural damage of adjacent properties. In current practice, not all water leaks can be detected in a timely and cost effective manner. This paper presents a study conducted for detection of water leaks in underground pipelines, and identification of their respective locations using thermography infrared camera. The paper describes the field work and the experimental protocol which were carried out over 2 years in three different locations in greater Montreal (Canada) area in order to investigate factors that affect the applicability and limitations of using IR camera in water leak detection. These factors beyond those studied in previous work carried out by American Water Works Association and National Research Council. The paper presents ...

An Automated Artificial Neural Network System for Land Use/Land Cover Classification from Landsat TM Imagery

This paper focuses on an automated ANN classification system consisting of two modules: an unsupervised Kohonen’s Self-Organizing Mapping (SOM) neural network module, and a supervised Multilayer Perceptron (MLP) neural network module using the Backpropagation (BP) training algorithm. Two training algorithms were provided for the SOM network module: the standard SOM, and a refined SOM learning algorithm which incorporated Simulated Annealing (SA). The ability of our automated ANN system to perform Land-Use/Land-Cover (LU/LC) classifications of a Landsat Thematic Mapper (TM) image was tested using a supervised MLP network, an unsupervised SOM network, and a combination of SOM with SA network. Our case study demonstrated that the ANN classification system fulfilled the tasks of network training pattern creation, network training, and network generalization. The results from the three networks were assessed via a comparison with reference data derived from the high spatial resolution Digital Colour Infrared (CIR) Digital Orthophoto Quarter Quad (DOQQ) data. The supervised MLP network obtained the most accurate classification accuracy as compared to the two unsupervised SOM networks. Additionally, the classification performance of the refined SOM network was found to be significantly better than that of the standard SOM network essentially due to the incorporation of SA. This is mainly due to the SA-assisted classification utilizing the scheduling cooling scheme. It is concluded that our automated ANN classification system can be utilized for LU/LC applications and will be particularly useful when traditional statistical classification methods are not suitable due to a statistically abnormal distribution of the input data.

Synergistic use of optical and InSAR data for urban impervious surface mapping: a case study in Hong Kong

A wide range of urban ecosystem studies, including urban hydrology, urban climate, land use planning and watershed resource management, require accurate and up‐to‐date geospatial data of urban impervious surfaces. In this study, the potential of the synergistic use of optical and InSAR data in urban impervious surface mapping at the sub‐pixel level was investigated. A case study in Hong Kong was conducted for this purpose by applying a classification and regression tree (CART) algorithm to SPOT 5 multispectral imagery and ERS‐2 SAR data. Validated by reference data derived from high‐resolution colour‐infrared (CIR) aerial photographs, our results show that the addition of InSAR feature information can improve the estimation of impervious surface percentage (ISP) in comparison with using SPOT imagery alone. The improvement is especially notable in separating urban impervious surface from the vacant land/bare ground, which has been a difficult task in ISP modelling with optical remote sensing data. In addition, the results demonstrate the potential to map urban impervious surface by using InSAR data alone. This allows frequent monitoring of world's cities located in cloud‐prone and rainy areas.

Discussion of ‘Some limitations in the interpretation of vertical stereo photographic images for a landslide investigation’ by A.B. Hart, J.S. Griffiths &amp; A.E. Mather, Quarterly Journal of Engineering Geology and Hydrogeology , 42, 21–30

Roly Edwards writes: Having been centrally involved in the early development of terrain evaluation for engineering practice there are two matters of fact, which are misreported in what is otherwise a useful contribution drawing attention to the value of the use of land surface evaluation techniques for land stability studies. First, in their resume and evaluation of the use of vertical stereoscopic aerial photography in the investigation of the nature and distribution of landslide features in the Rio Aguas catchment in Spain, the authors suggest that stereoscopic images in the non-visible part of the electromagnetic spectrum were unavailable until the launch of SPOT in 1986. I would remind them that false colour IR and multispectral stereoscopic aerial photography at 1:5000 and 1:10 000 scales was routinely available, at a cost, as long …

Assessing threats to pool-breeding amphibian habitat in an urbanizing landscape

Geographically-based threat assessments are important for identifying natural resources at risk, yet have rarely been applied to identify habitat conservation priorities for imperiled organisms at a local scale. Pool-breeding amphibians have complex life cycles that place them at risk from habitat loss and fragmentation both in wetlands and in adjacent uplands. Because the most rapidly growing cause of habitat degradation in North America has been urbanization, a threat analysis of pool-breeding amphibian habitat should both be dynamic, i.e., sensitive to land-use change, and comprehensive, recognizing traditional protected area networks as well as less formal conservation assets (e.g., land-use regulations). To assess threats to wood frogs ( Rana sylvatica) and spotted salamanders ( Ambystoma maculatum) in a rapidly urbanizing, forested region of New England (USA) we examined gaps in the current protection network, as well as changing human settlement patterns. We found that greater than 50% of 542 potential breeding pools delineated using low-level infra-red aerial photography (median area 379.5 m 2) were not represented on National Wetland Inventory (U.S.FWS) maps, and thus de facto at risk. Most importantly, conservation lands and regulatory protections failed to protect 46% of potential breeding pools and 80% of adjacent non-breeding habitat. While an assessment of human settlement patterns projected that only 5% of the region contained high quality amphibian habitat under acute development pressure, nearly half of the region (44.7%) had attained a moderate threat level, highlighting the importance of conservation planning during early stages of urbanization. We conclude by illustrating the role for multiple conservation strategies when protecting functional landscapes for pool-breeding amphibians.

Comparison of Airborne Multispectral and Hyperspectral Imagery for Estimating Grain Sorghum Yield

Both multispectral and hyperspectral images are being used to monitor crop conditions and map yield variability, but limited research has been conducted to compare these two types of imagery for assessing crop growth and yields. The objective of this study was to compare airborne multispectral imagery with airborne hyperspectral imagery for mapping yield variability in grain sorghum fields. Airborne color-infrared (CIR) imagery and airborne hyperspectral imagery along with yield monitor data collected from four fields were used in this study. Three-band imagery with wavebands corresponding to the collected CIR imagery and four-band imagery with wavebands similar to QuickBird satellite imagery were generated from the 102-band hyperspectral imagery. All four types of imagery (two actual and two simulated) were aggregated to increase pixel size to match the yield data resolution. Principal components and all possible normalized difference vegetation indices (NDVIs) were derived from each type of imagery and related to yield. Statistical analysis showed that the hyperspectral imagery accounted for more variability in yield than the other three types of multispectral imagery and that the best narrow-band NDVIs among the 5151 NDVIs derived from each hyperspectral image explained more variability than the best NDVIs derived from any of the actual or simulated multispectral images. These results indicate that hyperspectral imagery has the potential for improving yield estimation accuracy.

Digital Infrared and Ultraviolet Photography using Advanced Camera Services Modified Equipment

As part of a series of investigations into the utility of digital SLR cameras for IR and UV photography the authors tested a range of Nikon cameras modified by Advanced Camera Services, together with the company’s adapted electronic flash units and camera bracket.

Infra-red Photography

Infra-red Photography

Detection, Characterization, and Modeling Vegetation in Urban Areas From High-Resolution Aerial Imagery

Research in the area of 3-D city modeling from remote sensed data greatly developed in recent years with an emphasis on systems dealing with the detection and representation of man-made objects, such as buildings and streets. While these systems produce accurate representations of urban environments, they ignore information about the vegetation component of a city. This paper presents a complete image analysis system which, from high-resolution color infrared (CIR) digital images, and a Digital Surface Model (DSM), extracts, segments, and classifies vegetation in high density urban areas, with very high reliability. The process starts with the extraction of all vegetation areas using a supervised classification system based on a Support Vector Machines (SVM) classifier. The result of this first step is further on used to separate trees from lawns using texture criteria computed on the DSM. Tree crown borders are identified through a robust region growing algorithm based on tree-shape criteria. A SVM classifier gives the species class for each tree-region previously identified. This classification is used to enhance the appearance of 3-D city models by a realistic representation of vegetation according to the vegetation land use, shape and tree species.