J. H. Everitt

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.

Radiometric estimation of biomass and nitrogen content of Alicia grass

Abstract Hand-held MARK-II radiometric measurements were used to estimate biomass yields and nitrogen (N) content of Alicia grass (Cynodon spp.) plots having five levels of nitrogen fertilization. The radiometric RED (630- to 690-nm) and NIR (760- to 900-nm) measurements obtained from the plots were converted to reflectance factors and to perpendicular (PVI) and ratio (RVI = NIR/RED) vegetation indices and then correlated with grass biomass yield and N content. The coefficients of determination (r2) in estimating biomass yield for the RED and NIR reflectance factors were 0.04 and 0.73, 1 respectively, and for PVI and RVI they were 0.68 1 and 0.61, 1 respectively. The correlations for N content were 0.02, 0.71, 1 0.69, 1 and 0.60, 1 respectively. Since beef cattle protein needs are related to grass N content these results may be useful to operational rangeland remote sensing programs for estimating animal carrying capacity using satellite data.

Using airborne middle-infrared (1.45-2.0 μm) video imagery for distinguishing plant species and soil conditions

Abstract This paper describes the use of a black-and-white visible/infrared (0.4–2.4 μm) sensitive video camera, filtered to record radiation within the 1.45–2.0 μm middle-infrared water absorption region, for discriminating among plant species and soil conditions. The camera provided adequate quality airborne imagery that distinguished the succulent plant species onions ( Allium cepum L.) and aloe vera ( Aloe barbadensis Mill.) from nonsucculent plant species. Moreover, wet soil, dry crusted soil, and dry fallow soil could be differentiated in middle-infrared video images. Succulent plants, however, could not be distinguished from wet soil or water. These results show that middle-infrared video imagery has potential use for remote sensing research and applications.

Using High Resolution Satellite Imagery to Map Black Mangrove on the Texas Gulf Coast

Abstract QuickBird false color satellite imagery was evaluated for distinguishing black mangrove [Avicennia germinans (L.) L.] populations on the south Texas Gulf Coast. The imagery had three bands (green, red, and near-infrared) and contained 11-bit data. Two subsets of the satellite image were extracted and used as test sites. Supervised and unsupervised image analysis techniques were used to classify the imagery. For the supervised classification of site 1, black mangrove had a producers accuracy of 82.1% and a users accuracy of 95.8%, whereas for the unsupervised classification, black mangrove had a producers accuracy of 100% and a users accuracy of 60.9%. In the supervised classification of site 2, black mangrove had a producers accuracy of 91.7% and a users accuracy of 100%, whereas in the unsupervised classification, black mangrove had a producers accuracy of 100% and a users accuracy of 85.7%. These results indicate that QuickBird imagery combined with image analysis techniques can be used successfully to distinguish and map black mangrove along the south Texas Gulf Coast.

Use of Archive Aerial Photography for Monitoring Black Mangrove Populations

Abstract 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.

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

Abstract 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 producers and users 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 producers accuracy of 78.6% and a users 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.

Near-real-time video systems for rangeland assessment

Abstract This paper reviews the current capability of video imagery for rangeland management assessment. Three video systems are described and evaluated: 1) a black-and-white four-band system with visible/near-infrared (0.4-1.1 μm) sensitivity, 2) a selectable three-band color system, and 3) a black-and-white monoband system with midinfrared (1.45-2.0 μm) sensitivity. These systems have provided near-real-time imagery that could be useful to detect differences among many variables such as plant species, phytomass levels, fertilized and drought-stressed grass, heavy grazing, and burned areas. The computer compatibility of video imagery also has been demonstrated. Finally, results have shown that video systems may have considerable application to integrate the above-listed variables for rangeland resource management assessment.