Abstract
The overarching goal of this research was to explore accurate methods of mapping irrigated crops, where digital cadastre information is unavailable: (a) Boundary separation by object-oriented image segmentation using very high spatial resolution (2.5–5 m) data was followed by (b) identification of crops and crop rotations by means of phenology, tasselled cap, and rule-based classification using high resolution (15–30 m) bi-temporal data. The extensive irrigated cotton production system of the Khorezm province in Uzbekistan, Central Asia, was selected as a study region. Image segmentation was carried out on pan-sharpened SPOT data. Varying combinations of segmentation parameters (shape, compactness, and color) were tested for optimized boundary separation. The resulting geometry was validated against polygons digitized from the data and cadastre maps, analysing similarity (size, shape) and congruence. The parameters shape and compactness were decisive for segmentation accuracy. Differences between crop phenologies were analyzed at field level using bi-temporal ASTER data. A rule set based on the tasselled cap indices greenness and brightness allowed for classifying crop rotations of cotton, winter-wheat and rice, resulting in an overall accuracy of 80 %. The proposed field-based crop classification method can be an important tool for use in water demand estimations, crop yield simulations, or economic models in agricultural systems similar to Khorezm.
Highlights
Sustainable land and water management is essential for securing food production under the situation of climate change, decreasing water resources and growing population especially in the face of limited arable land [1]
Special emphasis was on an objective procedure for selecting appropriate segmentation settings
Focus is set on the evaluation of the segmentation parameters and the separation of agricultural fields from non-fields
Summary
Sustainable land and water management is essential for securing food production under the situation of climate change, decreasing water resources and growing population especially in the face of limited arable land [1]. Accurate crop distribution maps can substantially support this effort as they contribute to three important aspects: planning, modelling, and monitoring of land and water allocation in agriculture. For these applications, remote sensing has proven to be a valuable tool in the past decades. Multi-temporal methods are better suited for crop mapping because they consider the phenological development of crops [3,4]. Due to their high temporal resolution, medium spatial resolution sensors like AVHRR [5]
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