Using digital photographs and object-based image analysis to estimate percent ground cover in vegetation plots

Luscier, Jason D.; Thompson, William L.; Wilson, John Max; Gorham, Bruce E.; Drăguţ, Lucian

doi:10.1890/1540-9295(2006)4[408:udpaoi]2.0.co;2

Cited by 111 publications

(91 citation statements)

References 4 publications

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“…Thus, we assumed that the object-oriented classification would be a better approach than a pixel-based classification. The advantages of object-oriented classification using high-resolution image data have been reported by many studies [59][60][61][62][63]. However, determining appropriate segmentation parameters is a time-consuming process, generally based on trial and error evaluation to derive homogeneous image segments representing similar thematic units such as vegetation types [62,64,65].…”

Section: Methodsmentioning

confidence: 99%

Evaluation of Polarimetric SAR Decomposition for Classifying Wetland Vegetation Types

et al. 2015

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Abstract:The Florida Everglades is the largest subtropical wetland system in the United States and, as with subtropical and tropical wetlands elsewhere, has been threatened by severe environmental stresses. It is very important to monitor such wetlands to inform management on the status of these fragile ecosystems. This study aims to examine the applicability of TerraSAR-X quadruple polarimetric (quad-pol) synthetic aperture radar (PolSAR) data for classifying wetland vegetation in the Everglades. We processed quad-pol data using the Hong & Wdowinski four-component decomposition, which accounts for double bounce scattering in the cross-polarization signal. The calculated decomposition images consist of four scattering mechanisms (single, co-and cross-pol double, and volume scattering). We applied an object-oriented image analysis approach to classify vegetation types with the decomposition results. We also used a high-resolution multispectral optical RapidEye image to compare statistics and classification results with Synthetic Aperture Radar (SAR) observations. The calculated classification accuracy was higher than 85%, suggesting that the TerraSAR-X quad-pol SAR signal had a high potential for distinguishing different vegetation types. Scattering components from SAR acquisition were particularly advantageous for classifying mangroves along tidal channels. OPEN ACCESSRemote Sens. 2015, 7 8564We conclude that the typical scattering behaviors from model-based decomposition are useful for discriminating among different wetland vegetation types.

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Section: Methodsmentioning

confidence: 99%

Evaluation of Polarimetric SAR Decomposition for Classifying Wetland Vegetation Types

et al. 2015

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“…Efficient and automatic segmentation of vegetation from images of the ground is an important step for many applications such as weed detection for site-specific treatment (Onyango and Marchant, 2003;Tellaeche et al, 2008a,b;Burgos-Artizzu et al, 2009). Also the ground classification covers a number of ecologically relevant categories (Luscier et al, 2006).…”

Section: Problem Statementmentioning

confidence: 99%

Automatic segmentation of relevant textures in agricultural images

Guijarro

Pajares

Riomoros

et al. 2011

Computers and Electronics in Agriculture

298

122

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Keywords:Machine vision Image segmentation Texture identification in crops Automatic tasks in agriculture One important issue emerging strongly in agriculture is related with the automatization of tasks, where the optical sensors play an important role. They provide images that must be conveniently processed. The most relevant image processing procedures require the identification of green plants, in our experiments they come from barley and corn crops including weeds, so that some types of action can be carried out, including site-specific treatments with chemical products or mechanical manipulations. Also the identification of textures belonging to the soil could be useful to know some variables, such as humidity, smoothness or any others. Finally, from the point of view of the autonomous robot navigation, where the robot is equipped with the imaging system, some times it is convenient to know not only the soil information and the plants growing in the soil but also additional information supplied by global references based on specific areas. This implies that the images to be processed contain textures of three main types to be identified: green plants, soil and sky if any. This paper proposes a new automatic approach for segmenting these main textures and also to refine the identification of sub-textures inside the main ones. Concerning the green identification, we propose a new approach that exploits the performance of existing strategies by combining them. The combination takes into account the relevance of the information provided by each strategy based on the intensity variability. This makes an important contribution. The combination of thresholding approaches, for segmenting the soil and the sky, makes the second contribution; finally the adjusting of the supervised fuzzy clustering approach for identifying sub-textures automatically, makes the third finding. The performance of the method allows to verify its viability for automatic tasks in agriculture based on image processing.

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“…however, most such studies produced only general categories, such as vegetation and non-vegetation (laliberte et al, 2007). Since wildlife species usually differentiate between vegetation types in their diets (Krebs et al, 2003), categorizing percent plant cover at a more detailed level (e.g., by functional types or species groups, Chen et al, 2009b) (Definiens, 2003), several studies have measured percent cover for vegetation categories on digital photographs, with promising results (booth et al, 2005, 2006aluscier et al, 2006;laliberte et al, 2007;Booth and Cox, 2008). However, classification error resulting from spectral separation methods (vegMeasure, erDAS imagine [erDAS, 2010], eCognition) can cause confusion among like-colored species.…”

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confidence: 99%

Digital Photograph Analysis for Measuring Percent Plant Cover in the Arctic

Chen¹,

Chen²,

Leblanc³

et al. 2010

ARCTIC

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AbStrACt. long-term satellite remote sensing data, when properly calibrated and validated against ground monitoring, could provide valuable data sets for assessing climate change impacts on ecosystems, wildlife, and other important aspects of life in the Arctic. Percent plant cover is ideal for seasonal and long-term ground monitoring because it can be observed non-destructively and is closely related to other key ecosystem variables, such as biomass and leaf area index (lAi). Accurately measuring percent plant cover in the Arctic, however, has been a challenge. Advances in digital photography and imageprocessing techniques have provided the potential to measure vegetation cover accurately. in this paper we report an adapted method for quantifying percent plant cover based on plot digital photograph classification (PDPC). In this digital image analysis, the red, green, and blue image channels and the intensity, hue, and saturation image channels were used together to ensure more accurate cover measurement and labeling of plant species. We evaluated the accuracy of the PDPC method and two other techniques, visual estimate and digital grid overlay, by testing against artificial plots with known percent cover, by comparing with destructively measured lAi, and by comparing results of the three methods. our evaluation indicates that the PDPC method is the most accurate. In addition, PDPC has the advantages of being objective, quick in the field, and suitable for measuring percent plant cover in the Arctic at the level of functional types or species groups.Key words: percent plant cover, Arctic, visual estimate, digital photograph, image classification, LAI rÉSuMÉ. lorsqu'elles sont bien calibrées et qu'elles sont validées contre le dépistage terrestre, les données résultant de la télédétection satellitaire à long terme pourraient fournir d'importants ensembles de données en vue de l'évaluation des incidences du changement climatique sur les écosystèmes, la faune et d'autres aspects-clés de la vie dans l'Arctique. le pourcentage de couverture végétale est idéal pour le dépistage saisonnier et le dépistage terrestre à long terme parce qu'il peut être observé sans qu'il n'y ait de destruction et parce qu'il est étroitement lié à d'autres variables-clés se rapportant aux écosystèmes, comme la biomasse et l'indice de surface foliaire (iSF). toutefois, dans l'Arctique, la mesure exacte du pourcentage de couverture végétale représente un défi. Les progrès réalisés dans les domaines de la photographie numérique et des techniques de traitement d'images fournissent la possibilité de mesurer la couverture végétale avec précision. Dans cette communication, nous faisons état d'une méthode adaptée permettant de quantifier le pourcentage de couverture végétale en fonction de la classification de photographies numériques de parcelles. Dans le cadre de l'analyse d'images numériques, les canaux rouges, verts et bleus des images ainsi que les canaux d'intensité, de tonalité et de saturation des images ont été utilisés pour donner lieu ...

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Using digital photographs and object-based image analysis to estimate percent ground cover in vegetation plots

Cited by 111 publications

References 4 publications

Evaluation of Polarimetric SAR Decomposition for Classifying Wetland Vegetation Types

Evaluation of Polarimetric SAR Decomposition for Classifying Wetland Vegetation Types

Automatic segmentation of relevant textures in agricultural images

Digital Photograph Analysis for Measuring Percent Plant Cover in the Arctic

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