James H. Everitt

Remote Sensing Techniques to Assess Water Quality

Remote sensing techniques can be used to monitor water quality parameters (i.e., suspended sediments (turbidity), chlorophyll, and temperature). Optical and thermal sensors on boats, aircraft, and satellites provide both spatial and temporal information needed to monitor changes in water quality parameters for developing management practices to improve water quality. Recent and planned launches of satellites with improved spectral and spatial resolution sensors should lead to greater use of remote sensing techniques to assess and monitor water quality parameters. Integration of remotely sensed data, GPS, and GIS technologies provides a valuable tool for monitoring and assessing waterways. Remotely sensed data can be used to create a permanent geographically located database to provide a baseline for future comparisons. The integrated use of remotely sensed data, GPS, and GIS will enable consultants and natural resource managers to develop management plans for a variety of natural resource management applications.

Applications and Research Using Remote Sensing for Rangeland Management

Rangelands are grasslands, shrublands, and savannas used by wildlife for habitat and livestock in order to produce food and fiber. Assessment and monitoring of rangelands are currently based on comparing the plant species present in relation to an expected successional end-state defined by the ecological site. In the future, assessment and monitoring may be based on indicators of ecosystem health, including sustainability of soil, sustainability of plant production, and presence of invasive weed species. USDA Agricultural Research Service (ARS) scientists are actively engaged in developing quantitative, repeatable, and low-cost methods to measure indicators of ecosystem health using remote sensing. Noxious weed infestations can be determined by careful selection of the spatial resolution, spectral bands, and timing of image acquisition. Rangeland productivity can be estimated with either Landsat or Advanced Very High Resolution Radiometer data using models of gross primary production based on radiation use efficiency. Lidar measurements are useful for canopy structure and soil roughness, indicating susceptibility to erosion. The value of remote sensing for rangeland management depends in part on combining the imagery with other spatial data within geographic information systems. Finally, ARS scientists are developing the knowledge on which future range-land assessment and monitoring tools will be developed.

Using spectral vegetation indices to estimate rangeland productivity

Abstract Spectral vegetation indices (VI), that use combinations of photographic red (RED) and near‐infrared (NIR) remotely sensed radiances, were used to develop predictive relations for pholosynthetically active (green) biomass of rangeland plant canopies. Two basic types of soil adjusted VIs thai use multiplicative ratios and linear additive transformations of RED and NIR radiances were evaluated. Results showed that the perpendicular vegetation index (PVI), soil adjusted VI (SAVI), transformed soil adjusted VI (TSAVI), and soil adjusted ratio VI (SAVI2), were all equally related to green standing biomass. Soil moisture measurements significantly improved predictive relations for green biomass with VI. Thus, it is postulated that a thermal sensor, that could provide an independent estimate of soil moisture, might be useful for improving estimates of range productivity during periods where rainfall accumulations are temporally and geographically sporadic. These results indicated that most of the Vis tes...

Photographic and videographic observations for determining and mapping the response of cotton to soil salinity

Better ways are needed to assess the extent and severity of soil salinity in fields in terms of economic impact on crop production and effectiveness of reclamation efforts. Procedures to help meet these needs were developed from soil salinity, plant height and boll counts, and digitized color infrared aerial photography and videography acquired during midboll set development stage for four salt-affected cotton (Gossypium hirsutum, L.) fields in the San Joaquin Valley of California. Unsupervised classijication procedures were used to produce seven-category spectral maps by field. Regression equations were developed from salinity measurements in the surface 30 cm (EC1) at 100-200 sample sites per field and the photography and videography digital counts at those same sites. The equations were used to estimate the salinity of each of the approximately 100,000 pixels per field, and the salinity categories corresponding to the spectral ones were mapped. The spectral classification maps and the estimated salinity maps corresponded well. Boll counts, made at about 20 sites perjield, were converted to lint yield and regressed on NDVl from both the photography and videography; the correlation coefficient (r) was 0.72 for video and 0.73 for the photographic data. Lint yields decreased by 43 f 10 kg ha-’ per dS mm1 increase in ECl, or

Measuring Canopy Structure with an Airborne Laser Altimeter

52 f 12 ha-’ at current market prices. Our results illustrate very practical ways to combine image analysis capability, spectral observations, and ground truth to map and quantify the severity of soil salinity and its effects on crops.

A CCD Camera‐based Hyperspectral Imaging System for Stationary and Airborne Applications

Quantification of vegetation patterns and properties is needed to determine their role on the landscape and to develop management plans to conserve our natural resources. Quantifying vegetation patterns from the ground, or by using aerial photography or satellite imagery is difficult, time consuming, and often expensive. Digital data from an airborne laser altimeter offer an alternative method to quantify selected vegetation properties and patterns of forest and range vegetation. Airborne laser data found canopy heights varied from 2 to 6 m within even-aged pine forests. Maximum canopy heights measured with the laser altimeter were significantly correlated to measurements made with ground-based methods. Canopy shape could be used to distinguish deciduous and evergreen trees. In rangeland areas, vegetation heights, spatial patterns, and canopy cover measured with the laser altimeter were significantly related with field measurements. These studies demonstrate the potential of airborne laser data to measure canopy structure and properties for large areas quickly and quantitatively.

Airborne laser measurements of rangeland canopy cover and distribution.

Abstract This paper describes a CCD (charge coupled device) camera‐based hyperspectral imaging system designed for both stationary and airborne remote sensing applications. The system consists of a high performance digital CCD camera, an imaging spectrograph, an optional focal plane scanner, and a PC computer equipped with a frame grabbing board and camera utility software. The CCD camera provides 1280(h) × 1024(v) pixel resolution and true 12‐bit dynamic range. The imaging spectrograph is attached to the camera via an adapter to disperse radiation into a range of spectral bands. The effective spectral range resulting from this integration is from 457.2 nm to 921.7 nm. The optional focal plane scanner can be attached to the front of the spectrograph via another adapter for stationary image acquisition. The camera and the frame grabbing board are connected via a double coaxial cable, and the utility software allows for complete camera control and image acquisition. The imaging system captures one line image for all the bands at a time and an aircraft or the focal plane scanner serves as a mobile platform to carry out pushbroom scanning in the along‐track direction. The horizontal and vertical binning capability of the camera makes it possible to obtain images with various spatial (160, 320, 640 and 1280 pixels in image width) and spectral (32, 64, 128, 256, 512 and 1024 bands) resolutions. Formulas are presented to show the relationships among binning factors, spatial resolutions, and flight height and speed. Images with all 24 possible combinations of binning factors were collected in a laboratory setting. Airborne images with 128 bands and a width of 640 pixels were also obtained from agricultural fields, rangelands and waterways. Procedures were developed to correct geometric distortions of the airborne hyperspectral imagery. Preliminary image acquisition testing trials indicate that this CCD camera‐based hyperspectral imaging system has potential for agricultural and natural resources applications.

Evaluating AISA+ hyperspectral imagery for mapping black mangrove along the South Texas Gulf coast.

Studies were made at 2 rangeland areas in south Texas to measure canopy cover and distribution with an airborne laser profiler. In a comparison of laser and ground measurements of canopy cover on the same eighteen 30.5-m segments at the Yturria area, laser measurements of canopy cover ranged from 1 to 89% and were correlated significantly (r2 = 0.89) with ground measurements (1 to 88%) on the same eighteen 30.5-m segments. Comparisons of laser measurements of canopy cover for 500- and 940-m segments with an average of three 30.5-m ground measurements of canopy cover made within these segments were also significantly correlated (r2 = 0.95). Topography, vegetation height, and spatial distribution of canopy cover for 6- to 7-km flightlines were also measured with the laser profiler. Airborne laser measurements of land surface features can provide quick and accurate measurements of canopy cover and distribution for large areas of rangeland. Accurate and timely data on the amount and distribution of plant cover are valuable for understanding vegetation characteristics, improving estimates of infiltration, erosion, and evapotranspiration for rangeland areas, and making decisions for managing rangeland vegetation.

Airborne Hyperspectral Imagery and Yield Monitor Data for Mapping Cotton Yield Variability

Mangrove wetlands are economically and ecologically important ecosystems and accurate assessment of these wetlands with remote sensing can assist in their management and conservation. This study was conducted to evaluate airborne AISA hyperspectral imagery and image transformation and classification techniques for mapping black mangrove populations on the south Texas Gulf coast. AISA hyperspectral imagery was acquired from two study sites and both minimum noise fraction (MNF) and inverse MNF transforms were performed. Four classification methods, including minimum distance, Mahalanobis distance, maximum likelihood, and spectral angle mapper (SAM), were applied to the noise-reduced hyperspectral imagery and to the band-reduced MNF imagery for distinguishing black mangrove from associated plant species and other cover types. Accuracy assessment showed that overall accuracy varied from 84 percent to 95 percent for site 1 and from 69 percent to 91 percent for site 2 among the eight classifications for each site. The MNF images provided similar or better classification results compared with the hyperspectral images among the four classifiers. Kappa analysis showed that there were no significant differences among the four classifiers with the MNF imagery, though maximum likelihood provided excellent overall and class accuracies for both sites. Producer’s and user’s accuracies for black mangrove were 91 percent and 94 percent, respectively, for site 1 and both 91 percent for site 2 based on maximum likelihood applied to the MNF imagery. These results indicate that airborne hyperspectral imagery combined with image transformation and classification techniques can be a useful tool for monitoring and mapping black mangrove distributions in coastal environments.

Mapping leafy spurge (Euphorbia esula) infestations using aerial photography and geographic information systems

Increased availability of hyperspectral imagery necessitates the evaluation of its potential for precision agriculture applications. This study examined airborne hyperspectral imagery for mapping cotton (Gossypium hirsutum L.) yield variability as compared with yield monitor data. Hyperspectral images were acquired using an airborne imaging system from two cotton fields during the 2001 growing season, and yield data were collected from the fields using a cotton yield monitor. The raw hyperspectral images contained 128 bands between 457 and 922 nm. The raw images were geometrically corrected, georeferenced and resampled to 1 m resolution, and then converted to reflectance. Aggregation functions were then applied to each of the 128 bands to reduce the cell resolution to 4 m (close to the cotton pickers cutting width) and 8 m. The yield data were also aggregated to the two grids. Correlation analysis showed that cotton yield was significantly related to the image data for all the bands except for a few bands in the transitional range from the red to the near-infrared region. Stepwise regression performed on the yield and hyperspectral data identified significant bands and band combinations for estimating yield variability for the two fields. Narrow band normalized difference vegetation indices derived from the significant bands provided better yield estimation than most of the individual bands. The stepwise regression models based on the significant narrow bands explained 61% and 69% of the variability in yield for the two fields, respectively. To demonstrate if narrow bands may be better for yield estimation than broad bands, the hyperspectral bands were aggregated into Landsat-7 ETM+ sensors bandwidths. The stepwise regression models based on the four broad bands explained only 42% and 58% of the yield variability for the two fields, respectively. These results indicate that hyperspectral imagery may be a useful data source for mapping crop yield variability.