An approach for delineating homogeneous zones by using multi-sensor data

Benedetto, Daniela De; Castrignanò, Annamaria; Rinaldi, Michele; Ruggieri, Sergio; Santoro, Franco; Figorito, Benedetto; Gualano, S.; Diacono, Mariangela; Tamborrino, R.

doi:10.1016/j.geoderma.2012.08.028

Cited by 61 publications

(50 citation statements)

References 25 publications

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“…Wolf [35] worked with four indices, namely NDWI, NDVI, Normalized Difference Soil Index (NDSI) from green and yellow, and Non-homogeneous Feature Difference (NHFD) from red-edge and coastal bands. De Benedetto et al [36] calculated NDVI from red and NIR1 bands and Normalized Difference Red Edge (NDRE) from NIR and red-edge bands. Eckert [37] referred to NDVI1 using NIR1 and red and NDVI2 using NIR2 and red.…”

Section: Study Areamentioning

confidence: 99%

High Spatial Resolution WorldView-2 Imagery for Mapping NDVI and Its Relationship to Temporal Urban Landscape Evapotranspiration Factors

et al. 2014

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Evapotranspiration estimation has benefitted from recent advances in remote sensing and GIS techniques particularly in agricultural applications rather than urban environments. This paper explores the relationship between urban vegetation evapotranspiration (ET) and vegetation indices derived from newly-developed high spatial resolution WorldView-2 imagery. The study site was Veale Gardens in Adelaide, Australia. Image processing was applied on five images captured from February 2012 to February 2013 using ERDAS Imagine. From 64 possible two band combinations of WorldView-2, the most reliable one (with the maximum median differences) was selected. Normalized Difference Vegetation Index (NDVI) values were derived for each category of landscape cover, namely trees, shrubs, turf grasses, impervious pavements, and water bodies. Urban landscape evapotranspiration rates for Veale Gardens were estimated through field monitoring using observational-based landscape coefficients. The relationships between remotely sensed NDVIs for the entire Veale Gardens and for individual NDVIs of different vegetation covers were compared with field measured urban landscape evapotranspiration rates. The water stress conditions experienced in January

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Section: Study Areamentioning

confidence: 99%

High Spatial Resolution WorldView-2 Imagery for Mapping NDVI and Its Relationship to Temporal Urban Landscape Evapotranspiration Factors

et al. 2014

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“…The experimental field comprised 1.8 ha, as approximately 360 m × 50 m. The soil was silty, and the field surface included a slight depression (maximum, 2 m) to the south-east (De Benedetto et al, 2013).…”

Section: Site and Soil Characterisationmentioning

confidence: 99%

Discrimination of tomato plants under different irrigation regimes: analysis of hyperspectral sensor data

et al. 2014

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The development and implementation of both economically and environmentally sustainable precision crop management systems can be greatly enhanced through the use of hyperspectral sensing. In this study, the potential of narrow‐waveband hyperspectral observations for the discrimination of water‐stressed tomato plants (Solanum lycopersicum L.) was investigated in a field experiment conducted in southern Italy. The tomato crop was grown in a 1.8‐ha test field that was split into two plots with different irrigation treatments: optimal and deficit water supplies, with the deficit supply using half of the water of the optimal supply in the second half of the crop growing cycle. Hyperspectral measurements were taken with a field spectroradiometer. To reduce the number of variables, principal component analysis was applied to each of six wavelength band sub‐intervals across the whole wavelength interval from 400 to 1000 nm. The retained principal components were then submitted to canonical discriminant analysis. Finally, the principal components and the canonical component were interpolated using multivariate and univariate geostatistical techniques, respectively, and then mapped. The two irrigation treatments produced different plant biomass and leaf area indices, which were higher under optimal than deficit water conditions, as was the plant water potential. These data show that the correlation between the individual bands varied during the crop cycle, so it was not feasible to choose a specific band to discriminate between the water treatments. However, we show that only a combination of all of the bands that use the full spectral information with differential weighting leads to clear discrimination of the two differently irrigated areas, with a mean accuracy of 75% to 77%. The processing of hyperspectral reflectance data using canonical discriminant analysis can thus provide valuable information for the agricultural producer for the identification of within‐field areas of plant stress, so as to implement site‐specific irrigation strategies. Copyright © 2014 John Wiley & Sons, Ltd.

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“…Song et al (2009) generated and compared delineation methods based on soil, nutrient and yield maps, a vegetation index (VI) distribution of one multispectral satellite scene, and combinations thereof. De Benedetto et al (2013) used a data fusion method of proximal and remote sensing information, while Boydell and McBratney (1999) based their delineation on modelled yield data from Landsat imagery.…”

Section: Introductionmentioning

confidence: 99%

Automatic delineation algorithm for site-specific management zones based on satellite remote sensing data

et al. 2017

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In light of the increasing demand for food production, climate change challenges for agriculture, and economic pressure, precision farming is an ever-growing market. The development and distribution of remote sensing applications is also growing. The availability of extensive spatial and temporal data-enhanced by satellite remote sensing and open-source policies-provides an attractive opportunity to collect, analyze and use agricultural data at the farm scale and beyond. The division of individual fields into zones of differing yield potential (management zones (MZ)) is the basis of most offline and mapoverlay precision farming applications. In the process of delineation, manual labor is often required for the acquisition of suitable images and additional information on crop type. The authors therefore developed an automatic segmentation algorithm using multi-spectral satellite data, which is able to map stable crop growing patterns, reflecting areas of relative yield expectations within a field. The algorithm, using RapidEye data, is a quick and probably low-cost opportunity to divide agricultural fields into MZ, especially when yield data is insufficient or non-existent. With the increasing availability of satellite images, this method can address numerous users in agriculture and lower the threshold of implementing precision farming practices by providing a preliminary spatial field assessment.

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An approach for delineating homogeneous zones by using multi-sensor data

Cited by 61 publications

References 25 publications

High Spatial Resolution WorldView-2 Imagery for Mapping NDVI and Its Relationship to Temporal Urban Landscape Evapotranspiration Factors

High Spatial Resolution WorldView-2 Imagery for Mapping NDVI and Its Relationship to Temporal Urban Landscape Evapotranspiration Factors

Discrimination of tomato plants under different irrigation regimes: analysis of hyperspectral sensor data

Automatic delineation algorithm for site-specific management zones based on satellite remote sensing data

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