Multispectral Aerial Imagery Research Articles

Tracking stormwater discharge plumes and water quality of the Tijuana River with multispectral aerial imagery

Spatial–temporal characteristics and environmental factors regulating the behavior of stormwater runoff from the Tijuana River in southern California were analyzed utilizing very high resolution aerial imagery, and time-coincident environmental and bacterial sampling data. Thirty nine multispectral aerial images with 2.1-m spatial resolution were collected after major rainstorms during 2003–2008. Utilizing differences in color reflectance characteristics, the ocean surface was classified into non-plume waters and three components of the runoff plume reflecting differences in age and suspended sediment concentrations. Tijuana River discharge rate was the primary factor regulating the size of the freshest plume component and its shorelong extensions to the north and south. Wave direction was found to affect the shorelong distribution of the shoreline-connected fresh plume components much more strongly than wind direction. Wave-driven sediment resuspension also significantly contributed to the size of the oldest plume component. Surf zone bacterial samples collected near the time of each image acquisition were used to evaluate the contamination characteristics of each plume component. The bacterial contamination of the freshest plume waters was very high (100% of surf zone samples exceeded California standards), but the oldest plume areas were heterogeneous, including both polluted and clean waters. The aerial imagery archive allowed study of river runoff characteristics on a plume component level, not previously done with coarser satellite images. Our findings suggest that high resolution imaging can quickly identify the spatial extents of the most polluted runoff but cannot be relied upon to always identify the entire polluted area. Our results also indicate that wave-driven transport is important in distributing the most contaminated plume areas along the shoreline.

Pre-visible Detection of Grub Feeding in Turfgrass using Remote Sensing

Japanese beetle grubs (Popillia japonica Newman) are rootfeeding pests of turfgrass in the Midwest and eastern United States causing millions of dollars of damage annually. To reduce unnecessary pesticide output by applying only where needed, turfgrass managers need a practical, noninvasive method to locate patchy infestations before unsightly damage has occurred. Spectrometer data and multispectral aerial imagery were evaluated for detecting pre-visible symptoms of grub damage in turfgrass, to facilitate sitespecific grub management. Using spectrometer reflectance data, first derivatives of reflectance, narrow-band vegetation indices, and linear combinations of multiple bands/indices, infested turfgrass plots were distinguished from un-infested plots 10 to16 days before visual differences appeared in the year when visual ratings were conducted. Pre-visible symptoms of grub feeding were not detected using the position of the red edge or edge of the red edge. Results using multispectral imagery were mixed, with early symptoms detected in only one of two years.

Automatic detection of shrub location, crown area, and cover using spatial wavelet analysis and aerial photography

Characterizing shrub-steppe rangeland condition often requires fine-scale measurement of individual plants across broad areas. Advances in remote sensing to develop improved algorithms to census and monitor individual rangeland plants using image data are important for improving the efficiency with which these critical areas are monitored. Here, we performed and evaluated the first test of spatial wavelet analysis (SWA) to automatically detect the location and crown diameter of individuals of two species of shrubs (Artemisia tridentata and Purshia tridentata). Additionally, we quantified the aggregated cover of these shrubs at the plot scale. High spatial resolution (0.25 and 1 m) multispectral aerial imagery and field-based vegetation measurements were collected in both spring and fall 2005. We found that image- and field-based measures of individual shrubs and their crown areas were highly correlated in the fall imagery (r = 0.89). Image-based SWA prediction of shrub cover at the plot level correlated better with field-based measures (r = 0.91) than did a traditional, image texture-based measure (r = 0.71). Analyses of imagery acquired in spring resulted in poorer relationships due to the decreased phenological contrast between shrubs and understory grasses in spring relative to fall. Statistical equivalence tests demonstrated that individual shrub crown areas derived from field data and SWA were statistically equivalent and not biased, but the SWA- and field-based assessments of plot-level cover were not statistically equivalent. These results represent progress towards developing automatic methods to analyze shrubs at the landscape scale using remotely sensed imagery.

Feasibility of using template-based and object-based automated detection methods for quantifying black and hybrid imported fire ant (Solenopsis invicta and S. invicta×richteri) mounds in aerial digital imagery

Imported fire ants construct earthen nests (mounds) that exhibit many characteristics which make them potentially good targets for remote sensing programs, including geographical orientation, topography, and bare soil surrounded by actively growing vegetation. Template-based features and object-based features extracted from aerial multispectral imagery of fire ant infested pastures were used to construct classifiers for automated fire ant mound detection. A classifier constructed using template-based features alone yielded a 79% probability of detection with a corresponding false positive rate of 9%. Addition of object-based features (compactness and symmetry) to the classifier yielded a 79% probability of detection with a corresponding false positive rate of 4%. Maintaining a 79% detection rate when applying the classifier to a second, unique pasture dataset with different seasonal and other environmental factors resulted in a false positive rate of 17.5%. Data demonstrate that automated detection of mounds with classifiers incorporating template- and object-based features is feasible, but it may be necessary to construct unique classifiers on a site-specific basis.

The Use of Early Season Multispectral Images for Weed Detection in Corn

The objective of this research was to determine the potential use of commercially available multispectral images to detect weeds at low densities during the critical period of weed control. Common lambsquarters seedlings were transplanted into plots of glyphosate-resistant corn at 0, 1, 2, and 4 plants/m2at two sites, Agronomy Center for Research and Extension (ACRE) and Meig's Horticultural Research Farm at the Throckmorton–Purdue Agricultural Center (TPAC), in Indiana. Aerial multispectral images (12 to 16 cm pixel resolution) were taken 18 and 32 days after planting (DAP) at ACRE and 19 and 32 DAP at TPAC. Corn and common lambsquarters could not be reliably detected and differentiated at either site when weeds were 9 cm or less in height. However, economic threshold densities (2 and 4 plants/m2) of common lambsquarters could be distinguished from weed-free plots at TPAC when weeds were 17 cm in height. At this height, common lambsquarters plants were beyond the optimal height for glyphosate application, but could still be readily controlled with higher rates. Results from this study indicate that commercially available multispectral aerial imagery at current spatial resolutions does not provide consistently reliable data for detection of early season weeds in glyphosate-resistant corn cropping systems. Additional refinement in sensor spatial and spectral resolution is necessary to increase our ability to successfully detect early season weed infestations.

Mapping and quantifying the South African kelp resource

Two species of kelp are exploited commercially in South Africa. Use of beach-cast Laminaria pallida is limited, although it comprises the bulk of the biomass on the northern West Coast. Ecklonia maxima dominates the southern West Coast and provides most of the material for the South African kelp industry. Harvests of E. maxima fronds for abalone feed have reached 3 000–5 000 tonnes fresh weight (t f wt) y−1, whereas beachcast material of both species (about 1 000t dry wt y−1) is collected mainly for alginate extraction. For the first time, a South African kelp inventory has been compiled using all available data for the 900km-long West Coast (Cape Agulhas to the Orange River), although a few gaps remain. Beds of E. maxima and L. pallida reaching the surface at low spring tides were mapped using one or several methods: infrared aerial photography, digital multispectral aerial imagery, Landsat satellite imagery, and physical mapping with a hand-held GPS. The data are on a GIS database. Landsat 5 TM imagery could identify the presence of kelp beds, but only infrared aerial imagery and multispectral imaging at low spring tides accurately quantified surface areas of kelp beds. Biomass of the main kelp (E. maxima) is variable in space (between 3kg f wt m−2 and 24kg f wt m−2) and time (changes of up to 50%), making estimates for management difficult. Using an average biomass value of 12kg m−2, the total biomass of surface-reaching kelp beds on the West Coast is estimated to exceed 593 000t f wt, but extensive subsurface beds remain unquantified. Results are discussed in relation to sustainable limits to harvesting, gaps in knowledge, and the improved management of kelp resources.

Development and validation of canopy reflectance-based crop coefficient for potato

The present paper describes the development and validation of canopy reflectance-based crop coefficients for potato using canopy reflectance (measured using hand-held radiometers and high resolution multispectral digital imagery), and extensive crop biophysical sampling in selected potato growing fields in and around Kimberly, Idaho, during 1998 and 1999 seasons. Daily crop evapotranspiration was estimated using basal and canopy reflectance-based crop coefficients, and a hydrologic water balance was conducted in the plant root zone. Independent measurements of actual soil moisture measurements were made by neutron probe and gravimetric methods, and used to validate the simulated results. The results validate the reflectance-based crop coefficient method. High resolution multispectral aerial imagery was used to highlight the spatial variability of actual crop water demand in the study fields.

Estimating topsoil organic matter content in two experimental fields in Denmark using multi-spectral aerial imagery and topographic data

Geografisk Tidsskrift, Danish Journal of Geography 105(2):1–14, 2005 The potential use of spectral reflectance data and topographical measurements for the estimation of topsoil organic matter (SOM) content is investigated. Intensive measurements of soil texture and topography in two Danish fields combined with digital aerial photographs, representing blue, green, red and nearinfrared wavelength regions, form the basis of the investigation. Analyses of semi- variograms indicate similarity between the spatial variation of the spectral, topographic and textural measurements, and confirms that the sampling density is sufficient to account for the spatial variation of SOM within each field. Multivariate statistics were applied to investigate the potential of spectral reflectance data, including selected spectral indices, and topographical data for prediction of SOM. The results show that the spectral reflectance data obtained within the visible and near infrared range are correlated with the SOM content in both fields, and that the spectral bands are almost co-linear. The relative importance of spectral reflectance and topographical data varied in the two fields, although the combined use of these data improved the estimation of SOM significantly in both fields. The data indicated that SOM content above 3.5% was rare in both fields although a high SOM content was found in one field, which was related to kettle holes characterised by poor drainage. The study focused on the estimation of SOM from zero to 3.5% within the 2 fields. The Predicted accuracy varied between 0.24 and 0.30% SOM, depending on the field and input parameters used in the model. The results also revealed that the data used in the investigation were insufficient to properly describe the variations of SOM within the fields.

Using Multispectral Aerial Imagery to Evaluate Crop Productivity

Remote sensing offers the potential to provide quantitative and timely information on agricultural crops and could be utilized for creating variable rate prescriptions for fertilizer and chemical applications. This would be particularly useful for mid-season management of crop inputs by allowing producers to better allocate needed inputs, thereby increasing efficiency and hopefully overall profitability. The objectives of this study were: (i) to verify a correlation between data gathered by a yield monitor and multispectral imagery, (ii) to evaluate the relationship between soil electrical conductivity and vegetative growth patterns, and (iii) to establish a method of producing a vegetation map from images that could be used for directed scouting and mid-season chemical application decisions. Multispectral aerial imagery was used to observe crop canopy spectral reflectance in a study for rice (), cotton (L.), and soybean () production fields in the Delta region of Arkansas. The images were acquired at various stages of crop growth and compared with ground reference observations consisting of soil electrical conductivity (ECa) and yield monitor data. Patterns were distinguishable with the use of the multispectral imagery along with some enhancements in a geographical information system (GIS). A classified, normalized difference vegetation index (NDVI) of the images was performed to further enhance the vegetative differences. The multispectral imagery proved to be a useful tool in assessing field variation through plant canopy reflectance. In the cotton and soybean production fields studied, yield variances due to soil characteristics were visible. Strong correlations, ranging from r = 0.26 to 0.83, were seen in classified images, yield maps, and ECa maps. However in the rice production field, final yield had little correlation with the images acquired throughout the season (r = 15), but the soil ECa map was related to the mid-season classified NDVI (r = 0.48).