Color-infrared Photography Research Articles

Color-Infrared Kite Aerial Photography with a Mirrorless Digital SLR Camera

Color-infrared (CIR) photography has been utilized often for many scientific and environmental applications since the mid-twentieth century. For the past two decades, the authors have developed techniques for CIR kite aerial photography, first with an analog (film) camera, then with an early digital camera, and now with a much improved digital camera. The Sony α6000 model is a compact, digital, mirrorless, single-lensreflex (SLR) camera that is specially modified for CIR; it records blue, green, and nearinfrared wavelengths that are depicted respectively as blue, green, and red in false-color format. Active, emergent vegetation appears orange in this format, in contrast to the red-pink color of vegetation in traditional CIR images. The Sony α6000 camera provides 24-megapixel images that are visually sharp and clear. Kites, blimps, and UAS (drones) have different technical characteristics and legal status and, thus, play distinct roles for small-format aerial photography.

The Biological Flora of Coastal Dunes and Wetlands:Avicennia germinans(L.) L.

ABSTRACT Lonard, R.I.; Judd, F.W.; Summy, K.R.; DeYoe, H., and Stalter, R., 2017. The biological flora of coastal dunes and wetlands: Avicennia germinans (L.) L. Avicennia germinans (L.) L. is a pantropical, subtropical, and occasionally warm-temperate mangrove species that occurs on shorelines that have a broad horizontal tidal range. Also known as black mangrove, stands typically develop under anoxic, water-logged conditions in substrates of silt or clay. Black mangrove can tolerate salinity values ranging from 0 to 90 parts per thousand. Salt is excreted from salt glands on both leaf epidermal surfaces, and aerosol salt spray and salt crystals are frequently observed on the upper leaf epidermis. Avicennia germinans is viviparous. The embryos have no dormancy requirements, and there is no seed bank. This mangrove species has the northernmost distributional range of any mangrove species in North America. It occurs in Louisiana and northern Florida. Color-infrared photography and airborne video imagery te...

Estimating crown defoliation of Scots pine (Pinus sylvestris L.) trees using small format digital aerial images

This study focuses on the possibilities of using small format digital aerial images for the estimation of tree crown condition. The test area was located in the eastern part of Lithuania where Scots pine (Pinus sylvestris L.) trees prevail and was photographed using a Canon EOS-1DsMark II digital camera installed on-board a SkyArrow ultra-light aircraft. The camera lenses were adopted to capture images corresponding to conventional color-infrared photography. In addition, the test area was photographed using a large format digital frame aerial camera (Vexcel UltraCam D) installed on board a Rockwell Turbo Commander 690A high performance commuter aircraft. The ground sampling density of the images taken was around 9-10 cm. Crown defoliation was assessed in the field for more than 500 Scots pine trees located in 46 sample plots representing stands of trees that were either 65 years old or 170 years old. Spearman’s correlations coefficients were used to check for relationships between tree crown defoliation and image characteristics. The defoliation was also predicted using the non-parametric k-Nearest Neighbor method applied on data available from aerial images alone. The results were validated using the “Leave One Out” technique by comparing the obtained data with data from the field assessed defoliation rates. The prediction root mean square errors were calculated using data from the small format aerial images as being 11.5% for the younger trees, whereas those calculated using conventional aerial images were between 9.5 and 9.9%. The differences in predicted root mean square errors disappeared in the older stands and both methods produced errors of between 8.1 and 8.5%. Defoliation class was correctly predicted for approximately 84-88% of the older tree crowns and correctly for 75-85% of the younger tree crowns. These results showed that small format aerial images had the potential to predict defoliation in tree crowns and were comparable with results obtained using conventional aerial images. Their main advantage is that small format images are much cheaper to obtain than conventional images when the areas targeted are thousands of hectares in size.

Mapping Giant Reed (Arundo donax) Infestations along the Texas–Mexico Portion of the Rio Grande with Aerial Photography

AbstractGiant reed is an invasive weed throughout the southern half of the United States, with the densest stands growing along the coastal rivers of southern California and the Rio Grande in Texas. The objective of this study was to use aerial photography to map giant reed infestations and to estimate infested areas along the Texas–Mexico portion of the Rio Grande. Aerial color-infrared photographs were taken along the Rio Grande between Brownsville and El Paso, TX, in June and July 2002. Based on the aerial photographs and ground surveys, the portion of the river from San Ygnacio to Lajitas, which has a river length of 898 km (558 mi), was found to be infested with giant reed. To estimate infested areas along both sides of the river, 65 (13.5%) of the 480 aerial photographs taken between Lajitas and San Ygnacio were randomly selected. The aerial photographs were digitized, rectified to Google Earth imagery, and then classified using maximum-likelihood classification techniques. The infested areas on both sides of the river, as well as water area and river length, from each photographic image were determined. Based on the estimates from the 65 aerial photos, the ratio of giant reed area to water area and the ratio of giant reed area to river length were calculated. The total giant reed area along the Rio Grande between Lajitas and San Ygnacio was estimated to be 5,981 ha (14,779 ac) with 3,714 ha or 62% on the U.S. side and 2,267 ha or 38% on the Mexican side. This study provides the first accurate estimates of giant reed infestations along the Texas–Mexico portion of the Rio Grande and will be useful for both land owners and government agencies for the estimation of water usage and economic loss and for the management and control of giant reed.

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.

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.

Acquisition of NIR-Green-Blue Digital Photographs from Unmanned Aircraft for Crop Monitoring

Payload size and weight are critical factors for small Unmanned Aerial Vehicles (UAVs). Digital color-infrared photographs were acquired from a single 12-megapixel camera that did not have an internal hot-mirror filter and had a red-light-blocking filter in front of the lens, resulting in near-infrared (NIR), green and blue images. We tested the UAV-camera system over two variably-fertilized fields of winter wheat and found a good correlation between leaf area index and the green normalized difference vegetation index (GNDVI). The low cost and very-high spatial resolution associated with the camera-UAV system may provide important information for site-specific agriculture.

MAPPING OF COLOPHOSPERMUM MOPANE USING LANDSAT TM IN EASTERN BOTSWANA

ABSTRACT The spatial distribution of a tree species, Colophospermum mopane is mapped at its southern limits in eastern Botswana. Its relationship with soil type distribution is also investigated. Methods applied in the exercise used the possibilities of integrated applications of geographic information systems and remote sensing to detect and delineate spectral classes of C. mopane and of soils. Landsat TM data for the wet and dry seasons were analysed and interpreted for soil and vegetation classification. Supervised classification of the wet season (19/04/93) image was carried out using land use/cover classes derived from Colour infrared (CIR) aerial photographs, flown during the wet season (April, 1996) over the study area. Classification of the dry season (19/10/84) image on the other hand was based on the existing Food and Agricultural Organisation (FAO) soil maps of the same area. Results show that CIR photographs can be reliably used in selecting training areas for the classification of Landsat TM imagery. Secondly, results also indicate that Landsat TM can be used to map the distribution pattern of mopane. The methods applied in this study and the tools used prove that the much needed inventorying of vegetation in Botswana can be carried out using remote sensing. Lastly, the study shows that spectral classes of soil and vegetation have a positive relationship.

Aerial Color Infrared Photography to Optimize In‐Season Nitrogen Fertilizer Recommendations in Winter Wheat

Remote sensing in the form of aerial color infrared (CIR) photography has been shown to be a useful tool for in‐season N management in winter wheat (Triticum aestivum L.). The objectives of this study were (i) to develop a methodology for predicting in‐season optimum fertilizer N rates for winter wheat at growth stage (GS) 30 directly from aerial CIR photography and (ii) to quantify how the relationships between these optimum N rates and spectral indices respond to different levels of biomass of the wheat crop. Field studies were conducted for three winter wheat growing seasons (2002–2004) over a wide range of soil conditions across North Carolina using a split‐split plot randomized complete block design. Different planting date–seeding rate (PDSR) combinations were applied to create a range of biomass levels at GS 30. Different levels of N were applied at GS 25 (N25) to create a range of N supply and winter wheat radiance, and at GS 30 (N30) to measure grain yield response to N30. Aerial CIR photographs were obtained at each site at GS 30 before N applications. Significant biomass response to PDSR and yield response to N25 and N30 were observed. Optimum N30 ranged from 0 to 124 kg ha−1 with a mean of 55 kg ha−1. Better prediction of optimum N30 rates were obtained with spectral indices calculated relative to high‐N reference strips compared to absolute bands or spectral indices. Biomass measured at GS 30 influenced the strength of the relationship between optimum N30 and spectral indices. When the GS‐30 biomass was >1000 kg ha−1, the best predictor of optimum N30 (R2 = 0.85) was a quadratic model based on measured winter wheat radiance relative to mean radiance in the G band for the high N reference strip (Rel GS).

Estimating cabbage physical parameters using remote sensing technology

Remote sensing has long been used as a tool to extract plant growth and yield information for many crops, but little research has been conducted on cabbage ( Brassica oleracea var. capitata L.) with this technology. The objective of this study was to evaluate aerial photography and field reflectance spectra for estimating cabbage physical parameters. An experiment was conducted on a cabbage field with 81 experimental plots to which different insecticide treatments were applied. Aerial color-infrared (CIR) photographs were taken from the field shortly before harvest. Meanwhile, field reflectance spectra and four plant physical parameters, including plant diameter, head diameter, plant weight and head weight, were measured from a total of 243 plants (three plants per plot). Plant area and spectral digital values for the near-infrared, red and green bands for each of the 243 plants were extracted from a digitized aerial CIR photograph. Four different vegetation indices, including the normalized difference vegetation index (NDVI), were calculated. Correlation analysis showed that the cabbage physical parameters were significantly related to the photo-derived plant area and spectral variables. Regression analysis showed that head weight was linearly related to plant area with an r 2 value of 0.91 and quadratically related to NDVI with an r 2 value of 0.66. Stepwise regression performed on cabbage head weight and 601 bands from 400 to 1000 nm in the field reflectance spectra revealed that 71% of the variability in head weight could be explained by eight significant bands in the spectra. As an application example, cabbage yield estimated from photo-derived plant area in each plot was used to compare the differences among 16 treatments. These results indicate that remote sensing can be a useful tool for evaluating cabbage growth and yield variations.

Mapping broom snakeweed through image analysis of color-infrared photography and digital imagery

A study was conducted on a south Texas rangeland area to evaluate aerial color-infrared (CIR) photography and CIR digital imagery combined with unsupervised image analysis techniques to map broom snakeweed [Gutierrezia sarothrae (Pursh.) Britt. and Rusby]. Accuracy assessments performed on computer-classified maps of photographic images from two sites had mean producer's and user's accuracies for broom snakeweed of 98.3 and 88.3%, respectively; whereas, accuracy assessments performed on classified maps from digital images of the same two sites had mean producer's and user's accuracies for broom snakeweed of 98.3 and 92.8%, respectively. These results indicate that CIR photography and CIR digital imagery combined with image analysis techniques can be used successfully to map broom snakeweed infestations on south Texas rangelands.

Evaluation of Color-Infrared Photography and Digital Imagery to Map Black Mangrove on the Texas Gulf Coast

A study was conducted on the south Texas Gulf Coast to evaluate color-infrared (CIR) aerial photography and CIR true digital imagery combined with unsupervised image analysis techniques to distinguish and map black mangrove [Avicennia germinans (L.) L.] populations. Accuracy assessments performed on computer-classified maps of photographic and digital images of the same study site had both producer's and user's accuracies of 100% for black mangrove. An accuracy assessment performed on a computer-classified map of a digital image only of a second study site had a producer's accuracy of 78.6% and a user's accuracy of 100%. These results indicate that CIR photography and digital imagery combined with image analysis techniques can be used successfully to distinguish and quantify the extent of black mangrove along the south Texas Gulf Coast.

Canopy Spectra and Remote Sensing of Ashe Juniper and Associated Vegetation

A study was conducted in central Texas to determine the potential of using remote sensing technology to distinguish Ashe juniper (Juniperus ashei Buchholz) infestations on rangelands. Plant canopy reflectance measurements showed that Ashe juniper had lower near-infrared reflectance than other associated woody plant species and lower visible reflectance than mixed herbaceous species in spring and summer. Ashe juniper could be distinguished on color-infrared aerial photographs acquired in March, April, June, and August and on QuickBird false color satellite imagery obtained in June, where it had a distinct dark reddish-brown tonal response. Unsupervised classification techniques were used to classify aerial photographic and satellite imagery of study sites. An accuracy assessment performed on a computer classified map of a photographic image showed that Ashe juniper had producer's and user's accuracies of 100% and 92.9%, respectively, whereas an accuracy assessment performed on a classified map of a satellite image of the same site showed that Ashe juniper had producer's and user's accuracies of 94.1% and 88.1%, respectively. Accuracy assessments performed on classified maps of satellite images of two additional study sites showed that Ashe juniper had producer's and user's accuracies that ranged from 87.1% to 96.4%. These results indicate that both color-infrared photography and false color satellite imagery can be used successfully for distinguishing Ashe juniper infestations.

Aerial Color Infrared Photography for Determining Early In‐Season Nitrogen Requirements in Corn

In‐season determination of corn (Zea mays L.) N requirements via remote sensing may help optimize N application decisions and improve profit, fertilizer use efficiency, and environmental quality. The objective of this study was to use aerial color‐infrared (CIR) photography as a remote‐sensing technique for predicting in‐season N requirements for corn at the V7 growth stage. Field studies were conducted for 2 yr at three locations, each with and without irrigation, in the North Carolina Coastal Plain. Experimental treatments were a complete factorial of four N rates at planting (NPL) and five N rates at V7 (NV7). Aerial CIR photographs were taken at each of the locations at V7 before N application. Optimum NV7 ranged from 0 to 207 kg N ha−1 with a mean of 67 kg N ha−1. Significant but weak correlations were observed between optimum NV7 rates and the band combinations relative green, Relative Green Difference Vegetation Index, and Relative Difference Vegetation Index as measured in CIR photos. High proportions of soil reflectance in the images early in the corn growing season (V7) likely confounded our attempts to relate spectral information to optimum NV7 rates. The primary obstacles to applying this technique early in the season are the use of relative digital counts or indices that require high‐N reference strips in the field and strong background reflectance from the soil. When the NPL treatments that were nonresponsive to NV7 (i.e., optimum NV7 = 0) were removed from the analysis, the normalized near infrared, the Green Difference Vegetation Index, the Green Ratio Vegetation Index, and the Green Normalized Difference Vegetation Index were the best predictors of optimum NV7 rate (r2 = 0.33).

Attempt to Validate a Remote Sensing‐Based Late‐Season Corn Nitrogen Requirement Prediction System

Recent research showed that corn (Zea mays L.) may respond profitably to N applied at tasseling (VT), and that aerial color-infrared (CIR) photography could be used to predict economic optimum N rates at VT using the green difference vegetation index (GDVI, near-infrared brightness minus green brightness) calculated relative to a high N reference strip (relative GDVI, RGDVI). This technique could be used by farmers to identify N stress and quantify N requirements. To validate this technique for practical application, experiments were conducted with different rates of N applied at planting and at VT on irrigated and non-irrigated sites in North Carolina during 2003. In both irrigated and non-irrigated systems, maximum yield potential was achieved with 200 lb of N per acre at planting. The difference between predicted and observed optimum N rate at VT ranged from -27 to 80 lb/acre. Greater differences between predicted and observed optimum N rate at VT occurred when N requirement was high, which was attributed to lower yield potential in 2003 compared to the years when the model was developed. Although the model tended to over-predict N rates, it did capture differences in N requirements across the range of conditions tested, indicating that this technique can be an effective tool to determine late-season corn N need. Differences between the estimated and actual N rates might be reduced by incorporating a method for determining yield potential.

Aerial Color Infrared Photography for Determining Late‐Season Nitrogen Requirements in Corn

Fast and accurate methods of determining in‐season corn (Zea mays L.) N requirements are needed to provide more precise and economical management and potentially decrease groundwater N contamination. The objectives of this study were (i) to determine if there is a response to late‐season N applied to corn at pretassel (VT) under irrigated and nonirrigated conditions, and (ii) to develop a methodology for predicting in‐season N requirement for corn at the VT stage using aerial color infrared (CIR) photography. Field studies were conducted for 3 yr over a wide range of soil conditions and water regimes in the North Carolina Coastal Plain. Different fertilizer N rates were applied (i) at planting (NPL) to create a range of N supply, corn color, and near‐infrared (NIR) radiance; and (ii) at VT (NVT) to measure yield response to NVT. Aerial CIR photographs were obtained for each site at VT before N application. Significant grain yield responses to NPL and NVT were observed. Economic optimum NVT rates ranged from 0 to 224 kg ha−1 with a mean of 104 kg ha−1. Better prediction of economic optimum NVT rates was obtained with spectral band combinations rather than individual bands, and improved when calculated relative to high‐N reference strips measured at VT. The best predictor of economic optimum NVT (R 2 = 0.67) was a linear‐plateau model based on corn color and NIR radiance expressed using the Green Difference Vegetation Index (GDVI) relative to high‐N reference strips (Relative GDVI, RGDVI).

Canopy spectra of giant reed and associated vegetation

This paper describes the spectral light reflectance characteristics of giant reed (Arundo donax L.) and the application of aerial color-infrared photography and videography for distinguishing infestations of this invasive plant species in Texas riparian areas. Airborne videography was integrated with global positioning system (GPS) and geographic information system (GIS) technologies for mapping the distribution of giant reed. Field spectral measurements showed that giant reed had higher near-infrared reflectance than associated plant species in summer and fall. Giant reed had a conspicuous pink image response on the color-infrared photography and videography. This allowed infestations to be quantified using computer analysis of the photographic and videographic images. Accuracy assessments performed on the classified images had user's and producer's accuracies for giant reed that ranged from 78% to 100%. Integration of the GPS with the video imagery permitted latitude-longitude coordinates of giant reed infestations to be recorded on each image. A long stretch of the Rio Grande in southwest and west Texas was flown with the photographic and video systems to detect giant reed infestations. The GPS coordinates on the color-infrared video scenes depicting giant reed infestations were entered into a GIS to map the distribution of this invasive weed along the Rio Grande.

Estimation of surface chlorophyll on an exposed mudflat using digital colour-infrared (CIR) photography

The usefulness of field-based digital Colour-InfraRed (CIR) photography to quantify concentrations of chlorophyll on the surface of exposed mudflats is investigated. Multiple images, each 626 mm by 467 mm, were acquired during Austral summertime using a Duncantech three-band CIR camera from two areas of mudflat in the upper reaches of Sydney Harbour. Sediment samples were obtained from within the field of view of the camera and their chlorophyll concentration was estimated spectrophotometrically. After the camera images were normalised to compensate for the effects of variations in the intensity of downwelling solar radiation, chlorophyll was estimated for each 0.9 mm square pixel using a suite of five different vegetation indices. Regression analysis was used to determine the strength of the relationship between the index values and the estimates of chlorophyll from the in situ samples. Indices constructed from near-infrared and red bands were found to have the strongest relationships with in situ chlorophyll estimates ( R 2 ranging from 0.28 to 0.79) and indices derived from near-infrared and green bands the weakest ( R 2 ranging from 0.16 to 0.22). The vegetation indices highlighted complex small-scale variability in chlorophyll distribution that was not evident in the original camera images. These findings indicate that field-based CIR photography will provide a useful tool for the non-destructive determination of benthic chlorophyll.

Canopy spectra of giant reed and associated vegetation

This paper describes the spectral light reflectance characteristics of giant reed (Arundo donax L.) and the application of aerial color-infrared photography and videography for distinguishing infestations of this invasive plant species in Texas riparian areas. Airborne videography was integrated with global positioning system (GPS) and geographic information system (GIS) technologies for mapping the distribution of giant reed. Field spectral measurements showed that giant reed had higher near-infrared reflectance than associated plant species in summer and fall. Giant reed had a conspicuous pink image response on the color-infrared photography and videography. This allowed infestations to be quantified using computer analysis of the photographic and videographic images. Accuracy assessments performed on the classified images had user's and producer's accuracies for giant reed that ranged from 78% to 100%. Integration of the GPS with the video imagery permitted latitude-longitude coordinates of giant reed infestations to be recorded on each image. A long stretch of the Rio Grande in southwest and west Texas was flown with the photographic and video systems to detect giant reed infestations. The GPS coordinates on the color-infrared video scenes depicting giant reed infestations were entered into a GIS to map the distribution of this invasive weed along the Rio Grande.

Canopy spectra of giant reed and associated vegetation

This paper describes the spectral light reflectance characteristics of giant reed (Arundo donax L.) and the application of aerial color-infrared photography and videography for distinguishing infestations of this invasive plant species in Texas riparian areas. Airborne videography was integrated with global positioning system (GPS) and geographic information system (GIS) technologies for mapping the distribution of giant reed. Field spectral measurements showed that giant reed had higher near-infrared reflectance than associated plant species in summer and fall. Giant reed had a conspicuous pink image response on the color-infrared photography and videography. This allowed infestations to be quantified using computer analysis of the photographic and videographic images. Accuracy assessments performed on the classified images had user's and producer's accuracies for giant reed that ranged from 78% to 100%. Integration of the GPS with the video imagery permitted latitude-longitude coordinates of giant reed infestations to be recorded on each image. A long stretch of the Rio Grande in southwest and west Texas was flown with the photographic and video systems to detect giant reed infestations. The GPS coordinates on the color-infrared video scenes depicting giant reed infestations were entered into a GIS to map the distribution of this invasive weed along the Rio Grande.