Chenghai Yang

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

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.

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.

Using High-Resolution Airborne and Satellite Imagery to Assess Crop Growth and Yield Variability for Precision Agriculture

With increased use of precision agriculture techniques, information concerning within-field crop yield variability is becoming increasingly important for effective crop management. Despite the commercial availability of yield monitors, many crop harvesters are not equipped with them. Moreover, yield monitor data can only be collected at harvest and used for after-season management. On the other hand, remote sensing imagery obtained during the growing season can be used to generate yield maps for both within-season and after-season management. This paper gives an overview on the use of airborne multispectral and hyperspectral imagery and high-resolution satellite imagery for assessing crop growth and yield variability. The methodologies for image acquisition and processing and for the integration and analysis of image and yield data are discussed. Five application examples are provided to illustrate how airborne multispectral and hyperspectral imagery and high-resolution satellite imagery have been used for mapping crop yield variability. Image processing techniques including vegetation indices, unsupervised classification, correlation and regression analysis, principal component analysis, and supervised and unsupervised linear spectral unmixing are used in these examples. Some of the advantages and limitations on the use of different types of remote sensing imagery and analysis techniques for yield mapping are also discussed.

Unmanned Aerial Vehicles for High-Throughput Phenotyping and Agronomic Research

Advances in automation and data science have led agriculturists to seek real-time, high-quality, high-volume crop data to accelerate crop improvement through breeding and to optimize agronomic practices. Breeders have recently gained massive data-collection capability in genome sequencing of plants. Faster phenotypic trait data collection and analysis relative to genetic data leads to faster and better selections in crop improvement. Furthermore, faster and higher-resolution crop data collection leads to greater capability for scientists and growers to improve precision-agriculture practices on increasingly larger farms; e.g., site-specific application of water and nutrients. Unmanned aerial vehicles (UAVs) have recently gained traction as agricultural data collection systems. Using UAVs for agricultural remote sensing is an innovative technology that differs from traditional remote sensing in more ways than strictly higher-resolution images; it provides many new and unique possibilities, as well as new and unique challenges. Herein we report on processes and lessons learned from year 1—the summer 2015 and winter 2016 growing seasons–of a large multidisciplinary project evaluating UAV images across a range of breeding and agronomic research trials on a large research farm. Included are team and project planning, UAV and sensor selection and integration, and data collection and analysis workflow. The study involved many crops and both breeding plots and agronomic fields. The project’s goal was to develop methods for UAVs to collect high-quality, high-volume crop data with fast turnaround time to field scientists. The project included five teams: Administration, Flight Operations, Sensors, Data Management, and Field Research. Four case studies involving multiple crops in breeding and agronomic applications add practical descriptive detail. Lessons learned include critical information on sensors, air vehicles, and configuration parameters for both. As the first and most comprehensive project of its kind to date, these lessons are particularly salient to researchers embarking on agricultural research with UAVs.

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

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.

Relationships Between Yield Monitor Data and Airborne Multidate Multispectral Digital Imagery for Grain Sorghum

Remote sensing imagery taken during a growing season not only provides spatial and temporal information about crop growth conditions, but also is indicative of crop yield. The objective of this study was to evaluate the relationships between yield monitor data and airborne multidate multispectral digital imagery and to identify optimal time periods for image acquisition. Color-infrared (CIR) digital images were acquired from three grain sorghum fields on five different dates during the 1998 growing season. Yield data were also collected from these fields using a yield monitor. The images and the yield data were georeferenced to a common coordinate system. Four vegetation indices (two band ratios and two normalized differences) were derived from the green, red, and near-infrared (NIR) band images. The image data for the three bands and the four vegetation indices were aggregated to generate reduced-resolution images with a cell size equivalent to the combines effective cutting width. Correlation analyses showed that grain yield was significantly related to the digital image data for each of the three bands and the four vegetation indices. Multiple regression analyses were also performed to relate grain yield to the three bands and to the three bands plus the four indices for each of the five dates. Images taken around peak vegetative development produced the best relationships with yield and explained approximately 63, 82, and 85% of yield variability for fields 1, 2, and 3, respectively. Yield maps generated from the image data using the regression equations agreed well with those from the yield monitor data. These results demonstrated that airborne digital imagery can be a very useful tool for determining yield patterns before harvest for precision agriculture.

Airborne Videography to Identify Spatial Plant Growth Variability for Grain Sorghum

Much research has focused on the use of intensive grid soil sampling and yield monitors to identify within-field spatial variability in precision farming. This paper reports on the use of airborne videography to identify spatial plant growth patterns for grain sorghum. Color-infrared (CIR) digital video images were acquired from two grain sorghum fields in south Texas several times during the 1995 and 1996 growing seasons. The video images were registered, and classified into several zones of homogeneous spectral response using an unsupervised classification procedure. Ground truthing was performed upon a limited number of sites within each zone to determine plant density, plant height, leaf area index, biomass, and grain yield. Results from both years showed that the digital video imagery identified within-field plant growth variability and that classification maps effectively differentiated grain production levels and growth conditions within the two fields. A temporal comparison of the images and classification maps indicated that plant growth patterns differed somewhat between the two successive growing seasons, though areas exhibiting consistently high or low yield were identified within each field.

COMPARISONS OF UNIFORM AND VARIABLE RATE NITROGEN AND PHOSPHORUS FERTILIZER APPLICATIONS FOR GRAIN SORGHUM

Variable rate fertilizer application has the potential to improve fertilizer use efficiency, increase economic returns, and reduce environmental impacts. This study was designed to examine differences in yield and economic returns between uniform and variable rate fertilizer applications. During the 1997 and 1998 growing seasons, a variable rate applicator, capable of varying two liquid fertilizers simultaneously, was used to evaluate three fertility strategies: conventional uniform N, uniform N and P, and variable rate N and P. The three treatments were assigned in six blocks within three 14–ha grain sorghum fields (two blocks in each field) in a randomized complete block design. Thirty–six soil samples were taken in a staggered systematic grid from each field, and levels of soil nutrients were determined. Application rate maps for the variable rate N and P treatment were generated based on a fixed yield goal and site–specific soil N and P levels across the experimental plots, while application rates for the uniform N and P treatment were calculated from the same yield goal and average soil N and P levels for all three fields. Yield monitor data indicated that the variable rate treatment resulted in significantly higher yields than the uniform N and P treatment for both years (400 kg/ha higher in 1997 and 338 kg/ha higher in 1998). Moreover, coefficients of variation of yield monitor data for the variable rate treatment were smaller than those for the two uniform rate treatments. A simple economic analysis showed that the variable rate treatment had positive relative economic returns over the uniform N and P treatment (

Mapping grain sorghum growth and yield variations using airborne multispectral digital imagery.

27/ha in 1997 and

Spectral analysis of coastal vegetation and land cover using AISA + hyperspectral data

23/ha in 1998). However, if additional costs for soil sampling, equipment, and data analysis associated with variable rate application were considered, these returns would be much lower or even negative. These results showed that variable rate fertilization can increase yield, reduce yield variability, and improve economic returns. More experiments are needed to evaluate the long–term agronomic, economic, and environmental viability of variable rate technology in the Rio Grande Valley of south Texas