Spatially Located Platform and Aerial Photography for Documentation of Grazing Impacts on Wheat

Louhaichi, Mounir; Borman, Michael M.; Johnson, Douglas E.

doi:10.1080/10106040108542184

Cited by 518 publications

(161 citation statements)

References 5 publications

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“…Estimates of greenness (% green vegetation cover) were calculated from the digital camera images using supervised classification technique in VegMeasure software Ò (Louhaichi et al 2001;Johnson et al 2009). VegMeasure is a DVCT that measures vegetation on the ground in a non-destructive manner.…”

Section: Monitoring Techniquesmentioning

confidence: 99%

A reliable and non-destructive method for estimating forage shrub cover and biomass in arid environments using digital vegetation charting technique

Louhaichi

Hassan²,

Clifton³

et al. 2017

Agroforest Syst

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Despite the importance of fodder shrubs to small ruminant diets and production in arid and semiarid ecosystems, they are often not considered when quantifying grazing land potential. This oversight is mainly due to the time consuming and costly traditional techniques used to estimate shrub biomass. The shrub fodder component should be measured to avoid underestimation of the carrying capacity of rangelands. In this study, we present a fast, reliable and non-destructive method to estimate canopy vegetation cover to obtain aboveground shrub biomass. The experiment was conducted under field conditions in northwest Syria, where seedlings of seven shrub species were monitored for one year: Atriplex leucoclada (Moq.) Boiss., A. halimus L., A. lentiformis (Torr.) S. Watson, A. canescens (Pursh) Nutt., A. nummularia Lindl., Salsola vermiculata L. and Haloxylon aphyllum (C.A. Meyer) Bunge. The experimental layout was a randomized complete block design with five replications. We explored the effectiveness of digital vegetation charting technique (DVCT) for estimating shrub canopy cover.Aboveground shrub biomass was clipped to estimate the dry matter (DM) weight per species and to determine its relationship to canopy cover. In this study, an estimate of greenness (percent green vegetation cover) was extracted by way of greenness algorithms. Simple linear regressions between vegetation cover and biomass for 210 plots were performed. The cover of the seven species differed (P \ 0.01): A. leucoclada had the highest vegetation cover (56%) and H. aphyllum the lowest (7%). Vegetation cover and DM biomass were positively correlated (P \ 0.01) with R-squared ranging from 0.66 (H. aphyllum) to 0.84 (S. vermiculata). Our method provided reasonable estimations of canopy coverage which could predict aboveground phytomass. We conclude that DVCT offers a rapid, reliable and consistent measurement of shrub cover and biomass provided that shrubs have open architecture. This study shows the potential of digital cameras and image processing to determine cover/biomass in a nondestructive, timely and cost efficient way.

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Section: Monitoring Techniquesmentioning

confidence: 99%

A reliable and non-destructive method for estimating forage shrub cover and biomass in arid environments using digital vegetation charting technique

Louhaichi

Hassan²,

Clifton³

et al. 2017

Agroforest Syst

Self Cite

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show abstract

“…Formula Normalized Difference Vegetation Index (NDVI) [44] (NIR − R)/(NIR + R) Green Leaf Index (GLI) [45] ( [46] EVI = 2.5 × (NIR −R)/(NIR +6 × R − 7.5 × B + 1) Soil Adjusted Vegetation Index (SAVI) [47] (…”

Section: Vegetation Index (Vi)mentioning

confidence: 99%

A Cloud-Based Multi-Temporal Ensemble Classifier to Map Smallholder Farming Systems

et al. 2018

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Smallholder farmers cultivate more than 80% of the cropland area available in Africa. The intrinsic characteristics of such farms include complex crop-planting patterns, and small fields that are vaguely delineated. These characteristics pose challenges to mapping crops and fields from space. In this study, we evaluate the use of a cloud-based multi-temporal ensemble classifier to map smallholder farming systems in a case study for southern Mali. The ensemble combines a selection of spatial and spectral features derived from multi-spectral Worldview-2 images, field data, and five machine learning classifiers to produce a map of the most prevalent crops in our study area. Different ensemble sizes were evaluated using two combination rules, namely majority voting and weighted majority voting. Both strategies outperform any of the tested single classifiers. The ensemble based on the weighted majority voting strategy obtained the higher overall accuracy (75.9%). This means an accuracy improvement of 4.65% in comparison with the average overall accuracy of the best individual classifier tested in this study. The maximum ensemble accuracy is reached with 75 classifiers in the ensemble. This indicates that the addition of more classifiers does not help to continuously improve classification results. Our results demonstrate the potential of ensemble classifiers to map crops grown by West African smallholders. The use of ensembles demands high computational capability, but the increasing availability of cloud computing solutions allows their efficient implementation and even opens the door to the data processing needs of local organizations.

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“…Positive values are classified as plants because they contain higher green levels than average red or blue (Louhaichi et al 2001). VM uses a binomial classification system with analysis results displayed as a black and white image.…”

Section: Line-point Intercept (Lpi)mentioning

confidence: 99%

“…To make automated cover measurements we used ''VegMeasure,'' v. 1.6 (VM), a software program developed at Oregon State University for measuring plant cover on rangeland (Louhaichi et al 2001;Johnson et al 2003). VM is designed to quantify green-leaf area in large batches of digital images.…”

Section: Line-point Intercept (Lpi)mentioning

confidence: 99%

The Accuracy of Ground-Cover Measurements

Booth¹,

Cox²,

Meikle³

et al. 2006

REM

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Ground cover is a key indicator of rangeland condition and influences rangeland management decisions, yet there have been few advances in ground-cover measurement methods. The advent of digital photography and automated image processing promise a revolution in the way ground cover is measured. To assess the potential for automation we compared conventional and automated methods for measuring ground cover against known artificial populations. The known populations were created from 20 nadir images of a Wyoming big sagebrush (Artemisia tridentata Nutt. ssp. wyomingensis Beetle & Young) vegetation type acquired with a 5-megapixel Olympus E20 digital single lens reflex camera mounted on an aluminum camera frame at 2 m above ground level. The images were converted to color, 2-dimensional images that no longer represented real-world conditions but had known cover values and conserved a simplified form of the pattern and spatial context of the plant community. These images were then printed at 1:1 scale to a 1 3 1-m poster. Posters were evaluated for color cover under laboratory conditions using the conventional techniques of steel-point frame, laser-point frame, line-point intercept, ocular estimation, and line intercept. Photographs of the posters were measured for color cover using standard and custom-created algorithms within the VegMeasure image analysis framework, and using the Digital Grid Overlay method. Results indicate that conventional techniques had significantly greater correlation (! 92% agreement of measured to known) than measurements from the algorithms used in the VegMeasure analysis (70%). The critical factor influencing accuracy of point-sampling methods was the area of the contact point for the given method. These findings provide an important measure of relative accuracy among methods for land managers and for researchers seeking to improve rangeland monitoring methods. ResumenLa cobertura basal es un indicador clave de la condició n del pastizal e influye en las decisiones de manejo del pastizal; sin embargo, ha habido pocos avances en los métodos de medició n de la cobertura basal. El advenimiento de la fotografía digital y el procesamiento automá tico de imá genes promete un revolució n en la forma en la que la cobertura basal es medida. Pare evaluar el potencial de automatizació n, comparamos métodos convencionales y automatizados para medir la cobertura basal, la comparació n se realizó en poblaciones artificiales de cobertura conocida. Las poblaciones artificiales fueron creadas a partir de 20 imá genes de baja altura de un tipo de vegetació n de ''Wyoming big sagebrush'' (Artemisia tridentata Nutt. ssp. wyomingensis Beetle & Young) tomadas con una cá mara digital Olympus E20 de 5 megapixeles con lente reflex simple montada en un marco de aluminio a dos metros de altura del nivel del suelo. Las imá genes se convirtieron a color y en imágenes de dos dimensiones que ya no representaban el mundo real, pero tenían valores conocidos de cobertura y conservaron una forma simplificada del pa...

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Spatially Located Platform and Aerial Photography for Documentation of Grazing Impacts on Wheat

Cited by 518 publications

References 5 publications

A reliable and non-destructive method for estimating forage shrub cover and biomass in arid environments using digital vegetation charting technique

A reliable and non-destructive method for estimating forage shrub cover and biomass in arid environments using digital vegetation charting technique

A Cloud-Based Multi-Temporal Ensemble Classifier to Map Smallholder Farming Systems

The Accuracy of Ground-Cover Measurements

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