Near-infrared Reflectance Values Research Articles

Correction of Canopy Shadow Effects on Reflectance in an Evergreen Conifer Forest Using a 3D Point Cloud

Correction of spectral reflectance for shadow and topography in optical remote sensing data is challenging. Here, we corrected for canopy self-shadowing in an evergreen conifer forest in mountainous terrain using a three-dimensional (3D) point cloud. In our approach, the surface was modeled from structure-from-motion processed images provided by an unmanned aerial vehicle; then, the relationship between the observed spectral reflectance of the Sentinel-2A/B multispectral instrument, and the simulated sunlit fraction (the percentage of the solar-illuminated area within a Sentinel-2 pixel) was determined based on a ray-tracing scheme using a 3D point cloud. Scene-based and pixel-based linear regressions were applied to remove canopy-shadow and topographic effects from satellite-observed reflectance. Scene-based regression resulted in large seasonal changes that caused overcorrection in winter. Pixel-based regression generated stable seasonal profiles in both the red and near-infrared reflectance values over the conifer canopy; however, excessively smoothed seasonal changes were implemented over deciduous vegetation. In both correction methods, the reflection of incident light by the canopy likely improved the correction accuracy by decreasing the contrast between illuminated and shaded pixels in summer and increasing it in winter. The results were also visually compared with those from the Sun-Canopy-Sensor with C (SCS+C) method and the Sentinel-2 Level-2A product. This research demonstrates the effectiveness of using a 3D point cloud to correct for self-shadowing and topographic effects on remotely sensed reflectance.

A novel method for automatic potato mapping using time series of Sentinel-2 images

Crop maps produced by remote sensing data play an important role in agricultural crop studies. Most of the crop mapping methods rely on field samples to train a model, which is a costly and time-consuming process. On the other hand, automatic crop mapping methods which are based on unique spectral characteristics are independent of ground truth data. Since most crops have specific spectral and temporal features during the growing season, this research aims at developing a new automatic method to discriminate potato from other crops using Sentinel-2 time series imageries. In this research, Crop type data of three study sites in Iran consisting of 2019 fields of potato and other crops, which were sampled by a GPS receiver were used. Moreover, an additional site in the United States comprised of 880 fields from Cropland Data Layer (CDL) in raster format, was also utilized. We employed 50% (292) of the Hamedan fields to train the model and the remaining 2607 fields from other sites and 50% from Hamedan were used to validate the model. Then, the temporal reflectance spectra of various crops and potato were extracted and considered. Results show that potato has four unique spectral characteristics which can be utilized to distinguish potato fields. These include the near-infrared reflectance values at the cultivation and harvest dates, variations of the near-infrared reflectance at the greenness peak time and the ratio of the near-infrared reflectance values to the red reflectance values at the greenness peak. Therefore, a novel feature was proposed based on a combination of the above spectral characteristics for discrimination of potato fields with a kappa coefficient of higher than 0.8 and an overall accuracy of better than 90%, in the four study sites.

Using GIS and Remote Sensing Techniques to Study Water Quality Changes and Spectral Analysis of Tigris River within Mosul City, North of Iraq

The aim of the study is the measuring of changes in the spectral reflectivity water quality, analyzing the seasonal difference of Tigris River within Mosul City in the north of Iraq using Geographic Information Systems (GIS) and remote sensing techniques during the period (2014-2018). For this paper, Satellite images of the 8 Landsat in 2018 for four seasons have been selected in order to study the seasonal changes on the river they took place during 2018. A total of ten sample datasets were taken at the upstream, midstream and downstream along the Tigris River. This research focuses on analyzing the locational variance of reflectance, analyzing seasonal difference, and finding modeling algal amount change. There are distinctive reflectance differences among the downstream, mid-stream and upstream areas. Red, green, blue and near-infrared reflectance values decreased significantly toward the upstream. Results also showed that reflectance values are significantly associated with the seasonal factor. In the case of long-term trends, reflectance values have slightly increased in the downstream, while decreased slightly in the mid-stream and upstream. The modeling of chlorophyll-a and Secchi disk depth implies that water clarity has decreased over time while chlorophyll-a amounts have decreased. The decreasing water clarity seems to be attributed to other reasons than chlorophyll-a.

The Use of Low-Altitude UAV Imagery to Assess Western Juniper Density and Canopy Cover in Treated and Untreated Stands

Monitoring vegetation characteristics and ground cover is crucial to determine appropriate management techniques in western juniper (Juniperus occidentalis Hook.) ecosystems. Remote-sensing techniques have been used to study vegetation cover; yet, few studies have applied these techniques using unmanned aerial vehicles (UAV), specifically in areas of juniper woodlands. We used ground-based data in conjunction with low-altitude UAV imagery to assess vegetation and ground cover characteristics in a paired watershed study located in central Oregon, USA. The study was comprised of a treated watershed (most juniper removed) and an untreated watershed. Research objectives were to: (1) evaluate the density and canopy cover of western juniper in a treated (juniper removed) and an untreated watershed; and, (2) assess the effectiveness of using low altitude UAV-based imagery to measure juniper-sapling population density and canopy cover. Ground- based measurements were used to assess vegetation features in each watershed and as a means to verify analysis from aerial imagery. Visual imagery (red, green, and blue wavelengths) and multispectral imagery (red, green, blue, near-infrared, and red-edge wavelengths) were captured using a quadcopter-style UAV. Canopy cover in the untreated watershed was estimated using two different methods: vegetation indices and support vector machine classification. Supervised classification was used to assess juniper sapling density and vegetation cover in the treated watershed. Results showed that vegetation indices that incorporated near-infrared reflectance values estimated canopy cover within 0.7% to 4.1% of ground-based calculations. Canopy cover estimates at the untreated watershed using supervised classification were within 0.9% to 2.3% of ground-based results. Supervised classification applied to fall imagery using multispectral bands provided the best estimates of juniper sapling density compared to imagery taken in the summer or to using visual imagery. Study results suggest that low-altitude multispectral imagery obtained using small UAV can be effectively used to assess western juniper density and canopy cover.

Satellite monitoring of harmful algal blooms in the Western Basin of Lake Erie: A 20-year time-series

Blooms of harmful cyanobacteria (cyanoHABs) have occurred on an annual basis in western Lake Erie for more than a decade. Previously, we developed and validated an algorithm to map the extent of the submerged and surface scum components of cyanoHABs using MODIS ocean-color satellite data. The algorithm maps submerged cyanoHABs by identifying high chlorophyll concentrations (>18 mg/m3) combined with water temperature >20 °C, while cyanoHABs surface scums are mapped using near-infrared reflectance values. Here, we adapted this algorithm for the SeaWiFS sensor to map the annual areal extents of cyanoHABs in the Western Basin of Lake Erie for the 20-year period from 1998 to 2017. The resulting classified maps were validated by comparison with historical in situ measurements, exhibiting good agreement (81% accuracy). Trends in the annual mean and maximum total submerged and surface scum extents demonstrated significant positive increases from 1998 to 2017. There was also an apparent 76% increase in year-to-year variability of mean annual extent between the 1998–2010 and 2011–2017 periods. The 1998–2017 time-series was also compared with several different river discharge nutrient loading metrics to assess the ability to predict annual cyanoHAB extents. The prediction models displayed significant relationships between spring discharge and cyanoHAB area; however, substantial variance remained unexplained due in part to the presence of very large blooms occurring in 2013 and 2015. This new multi-sensor time-series and associated statistics extend the current understanding of the extent, location, duration, and temporal patterns of cyanoHABs in western Lake Erie.

Spectral characterization of forest plantations with Landsat 8/OLI images for forest planning and management

Abstract: The objective of this work was to evaluate the use of Landsat 8/OLI images to differentiate the age and estimate the total volume of Pinus elliottii, in order to determine the applicability of these data in the planning and management of forest activity. Fifty-three sampling units were installed, and dendrometric variables of 9-and-10-year-old P. elliottii commercial stands were measured. The digital numbers of the image were converted into surface reflectance and, subsequently, vegetation indices were determined. Red and near-infrared reflectance values were used to differentiate the ages of the stands. Regression analysis of the spectral variables was used to estimate the total volume. Increase in age caused an addition in reflectance in the near-infrared band and a decrease in the red band. The general equation for estimating the total volume for P.elliottii had an R2adj of 0.67 with a Syx of 31.46 m3 ha-1. Therefore, the spectral data with medium spatial resolution from the Landsat 8/OLI satellite can be used to distinguish the growth stages of the stands and can, thus, be used in the planning and proper management of forest activity on a spatial and temporal scale.

LANDSAT BIG DATA ANALYSIS FOR DETECTING LONG-TERM WATER QUALITY CHANGES: A CASE STUDY IN THE HAN RIVER, SOUTH KOREA

Abstract. Landsat imagery satisfies the characteristics of big data because of its massive data archive since 1972, continuous temporal updates, and various spatial resolutions from different sensors. As a case study of Landsat big data analysis, a total of 776 Landsat scenes were analyzed that cover a part of the Han River in South Korea. A total of eleven sample datasets was taken at the upstream, mid-stream and downstream along the Han River. This research aimed at analyzing locational variance of reflectance, analyzing seasonal difference, finding long-term changes, and modeling algal amount change. There were distinctive reflectance differences among the downstream, mid-stream and upstream areas. Red, green, blue and near-infrared reflectance values decreased significantly toward the upstream. Results also showed that reflectance values are significantly associated with the seasonal factor. In the case of long-term trends, reflectance values have slightly increased in the downstream, while decreased slightly in the mid-stream and upstream. The modeling of chlorophyll-a and Secchi disk depth imply that water clarity has decreased over time while chlorophyll-a amounts have decreased. The decreasing water clarity seems to be attributed to other reasons than chlorophyll-a.

Lake ice phenology from AVHRR data for European lakes: An automated two-step extraction method

Several lake ice phenology studies from satellite data have been undertaken. However, the availability of long-term lake freeze-thaw-cycles, required to understand this proxy for climate variability and change, is scarce for European lakes. Long time series from space observations are limited to few satellite sensors. Data of the Advanced Very High Resolution Radiometer (AVHRR) are used in account of their unique potential as they offer each day global coverage from the early 1980s expectedly until 2022. An automatic two-step extraction was developed, which makes use of near-infrared reflectance values and thermal infrared derived lake surface water temperatures to extract lake ice phenology dates. In contrast to other studies utilizing thermal infrared, the thresholds are derived from the data itself, making it unnecessary to define arbitrary or lake specific thresholds. Two lakes in the Baltic region and a steppe lake on the Austrian–Hungarian border were selected. The later one was used to test the applicability of the approach to another climatic region for the time period 1990 to 2012. A comparison of the extracted event dates with in situ data provided good agreements of about 10 d mean absolute error. The two-step extraction was found to be applicable for European lakes in different climate regions and could fill existing data gaps in future applications. The extension of the time series to the full AVHRR record length (early 1980 until today) with adequate length for trend estimations would be of interest to assess climate variability and change. Furthermore, the two-step extraction itself is not sensor-specific and could be applied to other sensors with equivalent near- and thermal infrared spectral bands.

Nitrogen Content Estimation of Rice Crop Based on Near Infrared (NIR) Reflectance Using Artificial Neural Network (ANN)

Nitrogen content is an important indicator used for monitoring and management of plant due to its role in photosynthesis, productivity as well as its effect on carbon and oxygen cycle. The research aimed at estimation of nitrogen content of rice crop based on Near Infrared (NIR) reflectance using Artificial Neural Network (ANN). ANN is a non-linear modeling tools based on statistical data. Nitrogen content was measured by laboratory analysis, meanwhile, its spectral reflectance of NIR (700 – 1075nm) in the field was measured by using hand held spectroradiometer. Data were divided into 33 data training and 15 data testing using 3-fold cross validation. We found that organic molecules (nitrogen, water, etc) have specific absorption pattern in the NIR region. The experimental result shows that the comparison between measured and model estimation of Nitrogen content have RMSE of about 0.32. We conclude that NIR reflectance values can be used to predict nitrogen content using ANN method.

Seasonal spectral variation of Zostera noltii and its influence on pigment-based Vegetation Indices

The influence of phenological variations on the reflectance of the intertidal marine angiosperm Zostera noltii was studied using spectroradiometry and pigment analysis. Leaves were sampled each month from March to November and spectral reflectance was measured in the 400–900nm wavelength range in the laboratory for increasing biomass. High Performance Liquid Chromatography (HPLC) was used to assess the pigment composition and concentration of the leaves and their epiphytes. Several vegetation indices (VIs) were tested for their sensitivity to seasonal variations, from NDVI-like ratios to indices based on derivative analysis. Pigment concentration showed a seasonal effect, with the highest concentrations observed in summer, synchronous with the above-ground biomass peak. The seasonal variations were clearly visible in the leaf reflectance spectrum, showing for the same amount of vegetation, lower reflectance in summer for the visible wavelengths and a higher near-infrared (NIR) plateau compared to the other months. Indices based on the difference between the red and NIR reflectance values, typically all the NDVI-like ratios, were the most sensitive to seasonal variations in pigment concentration. In fact, seasonal effects were systematically reduced for VIs including a blue band correction, namely the modified Specific Ratio (mSR(705)), the modified Normalized Difference (mND(705)), the modified NDVIs for Landsat, FORMOSAT and narrow band NDVI (mNDVI(673)) as well as the Atmospherically Resistant Vegetation Index (ARVI). However, these indices showed a faster saturation for increasing seagrass biomass, except for the mSR(705), which was the least sensitive to biomass saturation. The interesting properties of this index suggest that it should now be tested for airborne or satellite remote sensing mapping of Z. noltii beds but it requires a high spectral resolution and cannot be applied to multispectral satellite images.

Mapping deciduous rubber plantations through integration of PALSAR and multi-temporal Landsat imagery

Due to increasing global demand for natural rubber products, rubber (Hevea brasiliensis) plantation expansion has occurred in many regions where it was originally considered unsuitable. However, accurate maps of rubber plantations are not available, which substantially constrain our understanding of the environmental and socioeconomic impacts of rubber plantation expansion. In this study we developed a simple algorithm for accurate mapping of rubber plantations in northern tropical regions, by combining a forest map derived from microwave data and unique phenological characteristics of rubber trees observed from multi-temporal Landsat imagery. Phenology of rubber trees and natural evergreen forests in Hainan Island, China, was evaluated using eighteen Landsat TM/ETM+ images between 2007 and 2012. Temporal profiles of the Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Land Surface Water Index (LSWI), and near-infrared (NIR) reflectance for rubber trees and natural forest were constructed. The results showed that rubber plantations are distinguishable from natural evergreen forests in two phenological phases: 1) during the defoliation (leaf-off) phase in late February–March, vegetation index (NDVI, EVI, LSWI) values were lower in rubber plantations than in natural evergreen forests; and 2) during the foliation (new leaf emergence) phase in late March–April, rubber plantations had similar NDVI and LSWI values but higher EVI and NIR reflectance values than in natural forests. Therefore, it is possible to delineate rubber plantations within forested landscapes using one to two optical images acquired in the defoliation and/or foliation period. The mapping technique was developed and applied in the Danzhou Region of Hainan. Phased Array type L-band Synthetic Aperture Radar (PALSAR) 50-m Orthorectified Mosaic images were used to generate a forest cover map and further integrated with the phenological information of rubber plantations extracted from Landsat TM images during the foliation phase. The resultant map of rubber plantations has high accuracy (both producer's and user's accuracy is 96%). This simple and integrated algorithm has the potential to improve mapping of rubber plantations at the regional scale. This study also shows the value of time series Landsat images and emphasizes imagery selection at appropriate phenological phase for land cover classification, especially for delineating deciduous vegetation.

A multi-scale approach to mapping effective Leaf Area Index in Boreal Picea mariana stands using high spatial resolution CASI imagery

This study evaluates the performance of a simple geometric-optics reflectance model, used in combination with multi-spectral clustering, to map spatial patterns of effective Leaf Area Index ( L e ) within Boreal Picea mariana stands. Two metre resolution Compact Airborne Spectrographic Imager (CASI) images, acquired during the winter to minimize variability in understory reflectance, are used to map L e over BOREAS northern and southern Old Black Spruce tower flux sites. A combined multi-spectral clustering and ray-tracing approach is used to map open areas in each site at 2 m scale. A modified version of the Forest Light Interaction Model (FLIM) is then applied over canopy areas using 30 m scale red and near-infrared reflectance values derived from CASI images. Comparison of the combined FLIM and clustering approach (FLIMCLUS) with surface L e measurements in areas with overstory cover indicate a R 2 of 0.67 for the SSA-OBS site and 0.16 for the NSA-OBS site. The poor NSAOBS performance may be due to the low observed range of L e along the transect selected since additional measurements in a sparse canopy area closely match FLIM estimates. The relative standard error at both sites is under 10% and is close to the 5% precision error in surface L e estimates. Comparison of FLIMCLUS with FLIM and the simple ratio indicate substantial differences over open areas where the latter methods map-zero L e values. Further validation over other study sites, including surface data mapping edges between canopy and open areas, is proposed. The FLIM-CLUS L e maps may be useful for testing scale dependent assumptions within remote sensing algorithms and ecosystem flux models applied to the study sites and similar Picea mariana stands.

Using Airborne Digital Imagery for Detecting Oak Wilt Disease.

Color-infrared (CIR) digital imagery was evaluated as a remote sensing tool for detecting oak wilt disease in live oak (Quercus fusiformis). Aerial CIR digital imagery and CIR photography were obtained concurrently of a live oak forested area in south-central Texas affected by oak wilt. Dead, diseased, and healthy live oak trees could generally be delineated as well in the digital imagery as in the CIR photography. Light reflectance measurements obtained in the field showed that dead, diseased, and healthy trees had different visible and near-infrared reflectance values.

Classifying the geometry of canopies from time variations of red and near-infrared reflectance

A new method is suggested for classifying the geometry of canopies from red and near-infrared reflectance values measured from a nadir view. The method relies on the fact that the slope of a very strong linear statistical relationship ( R 2 > 0.95) between two indices varies significantly with the structure of the canopy. The first index (RVI c) is the ratio between reflectance in the near-infrared (NIR) and in the red channels, where intercept ( b) of the soil line is substracted from the NIR: RVI c = (NIR - b) / Red. The second index (GEO) in a new index calculated from the indices (PVI and SLI) introduced by Richardson and Wiegand (1977; Wiegand and Richardson, 1982): GEO = (SLI 2 + PVI 2) / (SLI − PVI). GEO is much more sensitive to canopy architecture than is RVI c. Thus the ratio of RVI c vs GEO, or the slope in such graphs, increases as leaf angle increases. Applied to three wheat cultivars, the smallest slope of the RVI c vs. GEO is for the cultivar that differed from the others by a mean leaf angle of about 15° or less. The significance of the approach is that it helps identify leaf angle differences among observed canopies when leaf area index (LAI) is unknown but has a sufficient space variability. This method is illustrated by the RVI c-GEO relations within a durum wheat field in the south of France observed on two SPOT satellite scenes. An increase of leaf erectness between tillering stage and early grain filling phase (2 weeks after anthesis) is clearly shown by the evolution of the slope of the RVI c vs. GEO relation.

Reflectance factor retrieval from Landsat TM and SPOT HRV data for bright and dark targets

In recent years, there have been many land-surface studies based on visible and near-infrared reflectance values retrieved from the Landsat Thematic Mapper (TM) and SPOT High Resolution Visible (HRV) sensors. Retrieval of reflectance from satellite sensor digital count requires knowledge of the atmospheric conditions and the sensor absolute calibration. In most cases, atmospheric conditions are simulated with a radiative transfer code and sensor calibration coefficients are obtained from preflight sensor calibrations or in-flight calibrations over bright surfaces (such as White Sands, New Mexico, USA, or La Crau, France). Though these procedures are well accepted, there have been few studies specifically designed to validate the accuracy of such reflectance factor retrievals (RFR) for both bright and dark targets. Data from two experiments conducted in an agricultural region in central Arizona were analyzed to quantify the accuracy of RFR from the Landsat TM and SPOT HRV sensors. These data included measurements made with groundbased and aircraft-based four-band radiometers and the NASA Advanced Solid-State Array Spectrometer (ASAS) aboard a C130 aircraft, and TM and HRV images acquired at nadir and off-nadir viewing angles. Results showed that the off-nadir reflectance factors measured using ground- and aircraft-based instruments, including ASAS, were comparable. The RFR from the satellite-based TM and HR V sensors generally resulted in an overestimation of dark target reflectance (up to 0.05 reflectance in the visible) and an underestimation of bright target reflectance (up to 0.1 reflectance in the near-infrared). Even greater error was possible when RFR was based on outdated sensor calibrations, particularly those conducted prelaunch. There was supporting evidence from studies at three sites (White Sands, New Mexico; Maricopa, Arizona; and Walnut Gulch, Arizona) that the Landsat-5 TM sensor sensitivity may have degraded by as much as 20% from the prelaunch calibration. Regarding the potential error in RFR related to recent changes in the processing of Landsat TM data (Level-0 and Level-1) by EOSAT Corporation, we found that the Level-0 data was slightly greater (∼2 digital counts) than the Level-1 data for all bands and all targets in our study.

Monitoring the performance of a broad-based calibration for measuring the nutritive value of two independent populations of pasture using near infrared reflectance (NIR) spectroscopy

Near infrared reflectance (NIR) spectroscopy is a rapid and cost-effective method for the measurement of organic constituents of agricultural products. NIR is widely used to measure feed quality around the world and is gaining acceptance in Australia. This study describes the development of an NIR calibration to measure crude protein (CP), predicted in vivo dry matter digestibility (IVDMD) and neutral detergent fibre (NDF) in temperate pasture species grown in south-western Victoria. A subset of 116 samples was selected on the basis of spectral characteristics from 461 pasture samples grown in 1987-89. Several grass and legume species were present in the population. Stepwise multiple linear regression analysis was used on the 116 samples to develop calibration equations with standard errors of 0.8,2.3 and 2.2% for CP, NDF and IVDMD, respectively. When these equations were tested on 2 independent pasture populations, a significant bias existed between NIR and reference values for 2 constituents in each population, indicating that the calibration samples did not adequately represent the new populations for these constituents. The results also showed that the H statistic alone was inadequate as an indicator of equation performance. It was confirmed that it was possible to develop a broad-based calibration to measure accurately the nutritive value of closed populations of temperate pasture species. For the resulting equations to be used for analysis of other populations, however, they must be monitored by comparing reference and NIR analyses on a small number of samples to check for the presence of bias or a significant increase in unexplained error.

Space station negotiations successfully completed

In recent years, there have been many land-surface studies based on visible and near-infrared reflectance values retrieved from the Landsat Thematic Mapper (TM) and SPOT High Resolution Visible (HRV) sensors. Retrieval of reflectance from satellite sensor digital count requires knowledge of the atmospheric conditions and the sensor absolute calibration. In most cases, atmospheric conditions are simulated with a radiative transfer code and sensor calibration coefficients are obtained from preflight sensor calibrations or in-flight calibrations over bright surfaces (such as White Sands, New Mexico, USA, or La Crau, France). Though these procedures are well accepted, there have been few studies specifically designed to validate the accuracy of such reflectance factor retrievals (RFR) for both bright and dark targets. Data from two experiments conducted in an agricultural region in central Arizona were analyzed to quantify the accuracy of RFR from the Landsat TM and SPOT HRV sensors. These data included measurements made with groundbased and aircraft-based four-band radiometers and the NASA Advanced Solid-State Array Spectrometer (ASAS) aboard a C130 aircraft, and TM and HRV images acquired at nadir and off-nadir viewing angles. Results showed that the off-nadir reflectance factors measured using ground- and aircraft-based instruments, including ASAS, were comparable. The RFR from the satellite-based TM and HR V sensors generally resulted in an overestimation of dark target reflectance (up to 0.05 reflectance in the visible) and an underestimation of bright target reflectance (up to 0.1 reflectance in the near-infrared). Even greater error was possible when RFR was based on outdated sensor calibrations, particularly those conducted prelaunch. There was supporting evidence from studies at three sites (White Sands, New Mexico; Maricopa, Arizona; and Walnut Gulch, Arizona) that the Landsat-5 TM sensor sensitivity may have degraded by as much as 20% from the prelaunch calibration. Regarding the potential error in RFR related to recent changes in the processing of Landsat TM data (Level-0 and Level-1) by EOSAT Corporation, we found that the Level-0 data was slightly greater (∼2 digital counts) than the Level-1 data for all bands and all targets in our study.

Remote Sensing of Broom Snakeweed (Gutierrezia sarothrae) and Spiny Aster (Aster spinosus)

Field spectroradiometric plant canopy measurements showed that broom snakeweed [Gutierrezia sarothrae(Pursh.) Britt. and Rusby # GUESA] and spiny aster (Aster spinosusBenth. # ASTSN) had lower near-infrared (0.85-μm) reflectance than did other associated rangeland shrubs and herbaceous vegetation. The low near-infrared reflectances of both species were attributed to their erectophile (erect leaf/stem) canopy structures. These low near-infrared reflectance values caused broom snakeweed to have a dark-brown to black image on color-infrared aerial photos (0.50- to 0.90-μm), whereas spiny aster had a dark reddish-brown to black image. Other rangeland plant species had light-brown, red, or magenta images. Computer-based image analyses of color-infrared film positive transparencies showed that broom snakeweed and spiny aster infestations could be quantitatively differentiated from associated rangeland species. Computer analyses can permit “percent land area infested” estimates of broom snakeweed and spiny aster infestations on rangelands.