Remote Sensing of Shaded Area in Vineyards

Johnson, Lee F.; Scholasch, Thibaut

doi:10.21273/horttech.15.4.0859

Cited by 19 publications

(15 citation statements)

References 17 publications

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“…Average final plant populations and standard errors for sparse, typical, and dense subplots were 89 ±4, 164 ±3, and 291 ±10 plants m -2 for -2004, respectively. For 2004-2005, they averaged 79 ±4, 156 ±4, and 305 ±10 plants m -2 , respectively.…”

Section: Wheat Planting Post-plant Operations and Crop Emergencementioning

confidence: 98%

“…Over the years, remote sensing scientists have clearly established relationships between various multispectral vegetation indices (VIs), such as the normalized difference vegetation index (NDVI), derived from optical visible and near-infrared data, and various biophysical aspects of vegetation canopies, such as leaf area index (Moran et al, 1995), crop yield (Plant et al, 2000), and percent crop cover (Heilman et al, 1982). Additional research has shown that the multispectral VIs can provide realtime surrogates of crop coefficients for a variety of crops (Bausch, 1995;Neale et al, 2003;Hunsaker et al, 2005a;Johnson and Scholasch, 2005). Therefore, VIs obtained with remote sensing data potentially offer a means to infer in near real-time the spatial distribution of K cb across the landscape of a local field or on a broader farm-scale basis.…”

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confidence: 99%

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Wheat Irrigation Management Using Multispectral Crop Coefficients: I. Crop Evapotranspiration Prediction

Hunsaker¹,

Fitzgerald²,

French³

et al. 2007

Transactions of the ASABE

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A method widely used for irrigation management determines crop evapotranspiration (ET c) from reference evapotranspiration (ET o) calculations and estimated crop coefficients. However, standard time-based crop coefficients may fail to represent the actual crop water use, for example, when deviations in weather or agronomic constraints appreciably change crop development patterns from typical conditions. In this study, the FAO-56 dual crop coefficient procedures were applied during experiments with wheat to calculate the estimated ET c for irrigation scheduling. The objective of this research was to determine whether basal crop coefficients (K cb) determined from a normalized difference vegetation index (NDVI treatment) improve the prediction of ET c over a standard application with a locally developed time-based K cb curve (FAO treatment). The experiments conducted for two seasons in central Arizona included subtreatments, equally replicated within the NDVI and FAO treatments, of three plant densities (typical, dense, and sparse) and two nitrogen levels (high and low) to provide a range of crop development and water use conditions. The effects of plant density and N level resulted in significant differences in measured seasonal ET c. Large variations that occurred in the observed K cb and ET c trends between subtreatments were better correlated with the NDVI than the FAO treatment. The mean absolute percent difference for predicted ET c was significantly smaller for NDVI than FAO during both seasons. The treatment difference was 5% for the first season, but 10% for the second season when an unexpected early decline in ET c and K cb was effectively predicted by the NDVI treatment but not by the FAO treatment. NDVI appears to be a robust approach for K cb estimation of wheat, able to reliably predict actual ET c for both typical and abnormal water use conditions.

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Section: Wheat Planting Post-plant Operations and Crop Emergencementioning

confidence: 98%

mentioning

confidence: 99%

Wheat Irrigation Management Using Multispectral Crop Coefficients: I. Crop Evapotranspiration Prediction

Hunsaker¹,

Fitzgerald²,

French³

et al. 2007

Transactions of the ASABE

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“…For each acquisition date, images were captured under clear skies near solar noon to minimize the effect of shadowing between the vine rows. 40,41 Images were captured using a Hawkeye UAV platform (Kingsland, Texas, USA) 42 and two digital cameras. The Hawkeye is a battery-powered (electric) kitewing plane with a single propeller and a payload of ∼400 g. The Hawkeye used in this study had autopilot functionality (Ardupilot) 43 that, when enabled automatically, navigated to preprogrammed waypoints (X,Y,Z) using an on-board global positioning system (GPS) receiver.…”

Section: Uav Image Collectionmentioning

confidence: 99%

Object-based spatiotemporal analysis of vine canopy vigor using an inexpensive unmanned aerial vehicle remote sensing system

Mathews

2014

J. Appl. Remote Sens

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Remotely sensed imagery provides a rapid assessment of spatial variability in grapevine canopy vigor that correlates with crop performance. Unmanned aerial vehicles (UAVs) provide a low-cost image acquisition platform with high spatial and temporal resolutions. Using a UAV and digital cameras, aerial images of a Texas vineyard were captured at postflowering, veraison, and harvest. Imagery was processed to generate orthophotos in units of reflectance, which were then segmented to extract per-vine estimates of canopy area (planimetric extent) and normalized difference vegetation index (NDVI)-based canopy density. Derived canopy area and density values were compared to the harvest variables of number of clusters, cluster size, and yield to explore correlations. Planimetrically derived canopy area yielded significant, positive relationships, whereas NDVI-based canopy density exhibited no significant relationships due to sensor-related radiometric inaccuracy. A vine performance index was calculated to map spatial variation in canopy vigor for the entire growing season. Future management zones were delineated using spatial grouping analysis.

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“…Previous studies have shown that various spectral vegetation indices, calculated from visible and near-infrared (NIR) reflectance data, are linearly related to the amount of photosynthetically active radiation absorbed by plant canopies (Asrar et al, 1984;Daughtry et al, 1992;Goward and Huemmrich, 1992;Johnson and Scholasch, 2005). Spectral indices such as the normalized difference vegetation index (NDVI), derived as the ratio of the difference and sum of reflectance in the NIR and red spectral regions, can effectively track spatially variable crop canopy development for particular crops in real time.…”

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confidence: 99%

Remote Sensing of Canopy Cover in Horticultural Crops

Trout¹,

Johnson²,

Gartung³

2008

horts

118

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Canopy cover (CC) is an important indicator of stage of growth and crop water use in horticultural crops. Remote sensing of CC has been studied in several major crops, but not in most horticultural crops. We measured CC of 11 different annual and perennial horticultural crops in various growth stages on 30 fields on the west side of California's San Joaquin Valley with a handheld multispectral digital camera. Canopy cover was compared with normalized difference vegetation index (NDVI) values calculated from Landsat 5 satellite imagery. The NDVI was highly correlated and linearly related with measured CC across the wide range of crops, canopy structures, and growth stages (R2 = 0.95, P < 0.01) and predicted CC with mean absolute error of 0.047 up to effective full cover. These results indicate that remotely sensed NDVI may be an efficient way to monitor growth stage, and potentially irrigation water demand, of horticultural crops.

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Remote Sensing of Shaded Area in Vineyards

Cited by 19 publications

References 17 publications

Wheat Irrigation Management Using Multispectral Crop Coefficients: I. Crop Evapotranspiration Prediction

Wheat Irrigation Management Using Multispectral Crop Coefficients: I. Crop Evapotranspiration Prediction

Object-based spatiotemporal analysis of vine canopy vigor using an inexpensive unmanned aerial vehicle remote sensing system

Remote Sensing of Canopy Cover in Horticultural Crops

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