geometrically corrected, and rectified to the World Geodetic, Survey 1984 (WGS84) datum and the Universal Transverse, Mercator (UTM) coordinate system. 0 The results further showed that MLC is an effective classification algorithm for differentiating different crops within the study area. If the customer’s area of interest is not covered in the ImageLibrary, DigitalGlobe will task the image collection in one of three commercial tasking options. 0000044110 00000 n '1ď����>�� Ӯ�.Λb�4 The geometric correc-, tion accounted for internal detector geometry, optical, distortion, scan distortion, line-rate variations, and registra-, tion of the multispectral bands. �,�J$�.PZf 6��G�k8�S����G�]��� v? Reflectance was measured in 490 discrete narrow bands between 350 and 1,050 nm. Yield data were collected at harvest from the two fields using a cotton Vegetation indices including For the QuickBird imagery, all four principal components were significant in the, component was significant and accounted for 60% of the, nents were significant for field 1, the first principal, component explained 35% of the variability, and the. Digital CCD cameras for airborne, Bethesda, Md. on an 8.4 m grid for two cotton fields in south Texas in 2003. imagery, the best variable (GNDVI) explained 46% of the. 0000005350 00000 n Four vegetation indices (two band ratios and two normalized differences) were derived from the green, red, and near-infrared (NIR) band images. The objective of this study was to evaluate QuickBird satellite imagery for mapping yield variability in cotton fields. Vegetation indices and principal components were derived from the images at the three spatial resolutions. The crop was at the, early square to early boll stage when the QuickBird and, peak canopy cover. Yang et al. This satellite was an excellent source of environmental data useful for analyses of changes in land usage, agricultural and forest climates. Statistical analysis showed that the 10-m, four-band image and the aggregated 20-m and 30-m images explained 68, 76 and 83%, respectively, of the variation in yield for all the fields combined. In this study a multispectral Radiometer (MSR5) was used to monitor tobacco varieties response to N application levels. blue (450-520 nm), green (520-600 nm), red (630-690 nm), and NIR (760-900 nm). These sites had stable reflectance. Both the QuickBird and MegaPlus.  compared QuickBird imagery and airborne imagery for mapping grain sorghum yield patterns. 0000150167 00000 n 312 0 obj<>stream 0000014829 00000 n The objective of this study was to determine whether measurements based on the NDVI could provide information useful for site-specific management of cotton. Further research is needed to evaluate the, advantages and disadvantages of QuickBird imagery for, yield estimation as compared with other types of remote, We thank Rene Davis and Fred Gomez for acquiring the, assistance in image processing. Using a state-of-the-art BGIS 2000 sensor (PDF), QuickBird satellite collected image data to 0.65m pixel resolution degree of detail. These results demonstrated that airborne digital imagery can be a very useful tool for determining yield patterns before harvest for precision agriculture. A critical problem with OMNBR models was that of “over fitting” (i.e., using more spectral channels than experimental samples to obtain a highly maximum R2 value). Mapping crop yield variability is one important aspect of precision agriculture. Stepwise, the QuickBird imagery was classified into 2 to, types of imagery were determined based on. All rights reserved. Farm managers are becoming increasingly aware of the spa- tial variability in crop production with the growing availabil- ity of yield monitors. Launching the first generation of high spatial and spectral resolution remote sensing satellite at the beginning of the 21 st century provides the opportunity to have better understanding of crop stress and the extent of stress in a specific environment. for determining plant growth and yield patterns for within-field crop management. A QuickBird satellite image was taken, variety planted in the fields was FiberMax 832, an okra-leaf, The QuickBird imagery contained four spectral. Airborne three-band imagery with submeter resolution 0000006731 00000 n The results further showed that the QuickBird image successfully detected stress within field and local scales, and therefore can be a robust tool in identifying issues of crop management at a local scale. field 2. Yield data were collected from the two fields using a cotton yield monitor. The data for this study comes from ground-level hyperspectral reflectance measurements of cotton, potato, soybeans, corn, and sunflower. 0000002329 00000 n 57% of the variability for field 2. 2003 growing season. While many studies have obtained promising results, several inter- fering factors can limit approaches solely based on spectral response, including tillage conditions and crop residue. Often this variability can be related to differences in soil properties (e.g., texture, organic matter, salinity levels, and nutrient status) within the field. This work was carried out to assess the ability of hyperspectral and high spatial resolution remote sensing imagery to detect stress in wheat in the Nile Delta of Egypt. The vegetation indices were evaluated for the best performance to characterize yield. The QuickBird satellite is the first in a constellation of spacecraft that DigitalGlobe has developed that offers highly accurate, commercial high-resolution imagery of Earth. Results showed that cotton yield was significantly related to both types of image 289 0 obj <> endobj The variability can be caused by various production inputs such as soil properties, water management, and fertilizer application. Aerial photographs were taken of replicated Acala cotton field experiments in California in which the treatment was water or nitrogen stress level. Everitt, J. H., D. E. Escobar, I. Cavazos, J. R. Noriega, and M. R. Davis. 0000009238 00000 n The QuickBird imagery was aggregated by a factor of, 3 to increase the cell size to 8.4 m, which was approximately, twice the cutting width. Normalized dif, indices were defined as the blue NDVI or BNDVI =, (NIR+Red). Results indicated that ratio of vegetation index (RVI) had a close relationship with yield (R-2=0.47). within fields. Crop yield is one of the most important pieces of information for crop management in precision agriculture. This chapter provides an overview of UAS platforms and sensors, and flight planning and imagery acquisition, before moving on to consider stitching and ortho-rectification in UAS image processing. Remote sensing has been an integral part of precision agriculture since the farming technology started developing in the mid to late 1980s. Therefore, airborne multispectral and hyperspectral imaging systems have been more widely used for assessing within-field crop growth and yield variation. Airborne color-infrared (CIR) digital imagery was acquired from the field on three dates in each growing season and ground observations (plant populations, height, yield) were made at 29 sites within the field. Multiple comparisons were made, least significant difference (LSD) test. The effect of irrigation on vegetation indices was significant. 0000044758 00000 n This study illustrates practical ways to integrate airborne digital imagery with spatial information technology and ground observations to map plant growth conditions and yield variations within crop fields. of spectral data to grain yield variation. These images and the ground measurements were integrated within a GIS to document, interpret, and map within-season and across-season plant growth and yield variability. a cropping area in south Texas, USA was acquired in the 2003 growing season. A PF3000 cotton yield monitor (Ag Leader Technology, Ames, Iowa) integrated with an AgGPS 132 receiver, (Trimble Navigation, Ltd., Sunnyvale, Cal.) �x������- �����[��� 0����}��y)7ta�����>j���T�7���@���tܛ�`q�2��ʀ��&���6�Z�L�Ą?�_��yxg)˔z���çL�U���*�u�Sk�Se�O4?�c����.� � �� R� ߁��-��2�5������ ��S�>ӣV����d�`r��n~��Y�&�+`��;�A4�� ���A9� =�-�t��l�`;��~p���� �Gp| ��[`L��`� "A�YA�+��Cb(��R�,� *�T�2B-� 0000006102 00000 n 0000001736 00000 n The images clearly revealed plant growth patterns within and across the three growing seasons as well as differences between the two tillage systems. USING IN SITU HYPERSPECTRAL MEASUREMENTS AND HIGH RESOLUTION SATELLITE IMAGERY TO DETECT STRESS IN WHEAT IN EGYPT, High resolution satellite imaging sensors for precision agriculture, The use of unmanned aerial systems (UASs) in precision agriculture, Assessing flue-cured tobacco crop growth and biomass response to nitrogen application levels using canopy reflectance, Hyperspectral Imagery for Mapping Crop Yield for Precision Agriculture, Estimation of cotton yield with varied irrigation and nitrogen treatments using aerial multispectral imagery, In-field variability detection and spatial yield modeling for corn using digital aerial imaging, Remote and Ground-Based Sensor Techniques to Map Soil Properties, Relationship of spectral data to grain-yield variation, Mapping grain sorghum growth and yield variations using airborne multispectral digital imagery, Relationships between remotely sensed reflectance data and cotton growth and yield, Airborne multispectral imagery for mapping variable growing conditions and yields of cotton, grain sorghum, and corn, Hyperspectral Vegetation Indices and Their Relationships with Agricultural Crop Characteristics, Relationships Between Yield Monitor Data and Airborne Multidate Multispectral Digital Imagery for Grain Sorghum, Mapping Grain Sorghum Yield Variability Using Airborne Digital Videography, Airborne Videography to Identify Spatial Plant Growth Variability for Grain Sorghum, Cotton Disease Identification & Delineation Based on Remote Sensing, Image debating based on improved dark channel prior method for agriculture, Evaluating high resolution SPOT 5 satellite imagery to estimate crop yield, Airborne Hyperspectral Imagery for Mapping Crop Yield Variability, Using High Resolution QuickBird Satellite Imagery for Cotton Yield Estimation, Comparison of Airborne Multispectral and Hyperspectral Imagery for Estimating Grain Sorghum Yield. Map showing the study area (black) in the Rio Grade V, (four counties) of south Texas. 0000012963 00000 n and estimating crop yields (Yang and Anderson, Supervisory Soil Scientist, USDA-ARS Kika de la Garza, multispectral imagery has been used for estimat-, (<3%). Spectral bands and vegetation indices derived from, airborne multispectral imagery have been related to yield, monitor data for mapping yield variability for corn (Senay et, tion satellite image data are becoming commercially avail-, able and more earth-observation satellites carrying, necessary to evaluate this type of image data for yield, tives of this study were to: (1) correlate yield monitor data, with QuickBird imagery and airborne imagery to compare, both types of imagery for yield estimation, and (2) apply, unsupervised classification to QuickBird imagery to differ-, entiate cotton yield levels among different, A QuickBird image covering approximately an 11.35, loam and clay loam. was installed on, a John Deere 9960 4-row cotton picker for yield data, were mounted on two of the four basket ducts of the cotton, picker. Positioning System (GPS) receiver (Pathfinder Pro XRS, Trimble Navigation, Ltd., Sunnyvale, Cal.). to the spectral bands and vegetation indices for both the satellite and airborne imagery. at the maturing stage of cotton (Thenkabail et al., The finer resolution of the airborne MegaPlus imagery, yield and reflectance for the four spectral, among the respective zones for each of the three, because the aggregation significantly reduced, regression was applied to a full model with four PCs for the QuickBird imagery and with three PCs for the MegaPlus ima, Means for each classification within a column followed by the same letter are not significantly different at the 0.0001 probabi, classifications of QuickBird imagery for two cotton fields in south T. D. E., J. H. Everitt, J. R. Noriega, M. R. Davis, and I. D. E., J. H. Everitt, J. R. Noriega, I. Cavazos, and M. R. Bethesda, Md. bands. (PCs) for QuickBird and airborne MegaPlus imagery for two cotton fields in south Texas in 2003. models and all PCs remaining in the models were significant at the 0.0001 level.
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