Using image-analysis to quantify the horizontal vegetation pattern in two multi-species savanna grasslands

Fransen, Bart; Boer, Michiel R. de; Terlou, M; During, H. J.; Dijkman, W.

doi:10.1023/a:1009706621867

Cited by 4 publications

(1 citation statement)

References 28 publications

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“…Dock (Rumex obtusifolius L.) was detected in mixed grassland swards by recording images with a remote-controlled vehicle in the field, segmenting the images using a homogeneity threshold and defining objects and features by describing shape, colour and texture (Gebhardt & K€ uhbauch, 2007;Gebhardt, Schellberg, Lock, & K€ uhbauch, 2006). Bonesmo, Kaspersen, and Bakken (2004) developed an image processing system to estimate the canopy cover of white clover in a legume-grass mixture based on clover colour and morphological properties, and Fransen, de Boer, Terlou, During, and Dijkman (1998) used DIA to quantify the horizontal vegetation pattern in savanna grasslands. Based on results from a pot experiment, DIA was suggested as a method to assess the legume contribution in legume-grass mixtures (Himstedt, Fricke, & Wachendorf, 2009).…”

Section: Photographymentioning

confidence: 99%

Remote sensing as a tool to assess botanical composition, structure, quantity and quality of temperate grasslands

Wachendorf

Fricke

Möckel

2017

Grass and Forage Science

116

100

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Grassland systems frequently exhibit small‐scale botanical and structural heterogeneity with pronounced spatio‐temporal dynamics. These features present particular challenges for sensor applications, in addition to limitations posed by the high cost and low spatial resolution of many available remote‐sensing (RS) systems. There has been little commercial application of RS for practical grassland farming. This article considers the developments in sensor performance, data analysis and modelling over recent decades, identifies significant advances in RS for grassland research and practice and reviews the most important sensor types and corresponding findings in research. Beside improvements of single sensor types, the development of systems with complementary sensors is seen as a very promising research area, and one that will help to overcome the limitations of single sensors and provide better information about grassland composition, yield and quality. From an agronomic point of view, thematic maps of farm fields are suggested as the central outcome of RS and data analysis. These maps could represent the relevant grassland features and constitute the basis for various farm management decisions at strategic, tactical and operational levels. The overarching goal will be to generate low cost, appropriate and timely information that can be provided to farmers to support their decision‐making.

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